A critical feature of biological intelligence is its capacity for life-long learning Cichon_Gan_2015
– the ability to acquire new knowledge from a sequence of experiences to solve progressively more tasks, while maintaining performance on previous ones. This, however, remains a serious challenge for current deep learning approaches. While current methods are able to outperform humans on many individual problemsSilver_etal_2016 ; Mnih_etal_2015 ; He_etal_2015 , these algorithms suffer from catastrophic forgetting French_1999 ; McClelland_etal_1995 ; McCloskey_Cohen_1989 ; Ratcliff_1990 ; Goodfellow_etal_2013 . Training on a new task or environment can be enough to degrade their performance from super-human to chance level Rusu_etal_2016 . Another critical aspect of life-long learning is the ability to sensibly reuse previously learnt representations in new domains (positive transfer). For example, knowing that strawberries and bananas are not edible when they are green could be useful when deciding whether to eat a green peach in the future. Finding semantic homologies between visually distinctive domains can remove the need to learn from scratch on every new environment and hence help with data efficiency – another major drawback of current deep learning approaches Garnelo_etal_2016 ; Lake_etal_2016 .
But how can an algorithm maximise the informativeness of the representation it learns on one domain for positive transfer on other domains without knowing a priori what experiences are to come? One approach might be to capture the important structure of the current environment in a maximally compact way (to preserve capacity for future learning). Such learning is likely to result in positive transfer if future training domains share some structural similarity with the old ones. This is a reasonable expectation to have for most natural (non-adversarial) tasks and environments, since they tend to adhere to the structure of the real world (e.g. relate to objects and their properties) governed by the consistent rules of chemistry or physics. A similar motivation underlies the Minimum Description Length (MDL) principle rissanen1978modeling and disentangled representation learning Bengio_etal_2013 .
Recent state of the art approaches to unsupervised disentangled representation learning Higgins_etal_2017 ; Burgess_etal_2017 ; Kim_Mnih_2017 ; Kumar_etal_2018 use a modified Variational AutoEncoder (VAE) Kingma_Welling_2014 ; Rezende_etal_2014 framework to learn a representation of the data generative factors. These approaches, however, only work on independent and identically distributed (IID) data from a single visual domain. This paper extends this line of work to life-long learning from piece-wise stationary data, exploiting this setting to learn shared representations across domains where applicable. The proposed Variational Autoencoder with Shared Embeddings (VASE, see fig. 1B) automatically detects shifts in the training data distribution and uses this information to allocate spare latent capacity to novel dataset-specific disentangled representations, while reusing previously acquired representations of latent dimensions where applicable. We use latent masking and a generative “dreaming” feedback loop (similar to ramapuram2017lifelong ; Shin_etal_2017 ; Seff_etal_2017 ; Ans_Rousset_1997 ) to avoid catastrophic forgetting. Our approach outperforms ramapuram2017lifelong , the only other VAE based approach to life-long learning we are aware of. Furthermore, we demonstrate that the pressure to disentangle endows VASE with a number of useful properties: 1) dealing sensibly with ambiguous inputs; 2) learning richer representations through imagination-based exploration; 3) performing semantically meaningful cross-domain inference by ignoring irrelevant aspects of surface-level appearance.
2 Related work
The existing approaches to continual learning can be broadly separated into three categories: data-, architecture- or weights-based. The data-based approaches augment the training data on a new task with the data collected from the previous tasks, allowing for simultaneous multi-task learning on IID data Espeholt_etal_2018 ; Robins_1995 ; Ratcliff_1990 ; McClelland_etal_1995 ; furlanello2016active . The architecture-based approaches dynamically augment the network with new task-specific modules, which often share intermediate representations to encourage positive transfer Rusu_etal_2016 ; Parisotto_etal_2015 ; Ruvolo_Eaton_2013 . Both of these types of approaches, however, are inefficient in terms of the memory requirements once the number of tasks becomes large. The weights-based approaches do not require data or model augmentation. Instead, they prevent catastrophic forgetting by slowing down learning in the weights that are deemed to be important for the previously learnt tasks Kirkpatrick_etal_2017 ; Zenke_etal_2017 ; Nguyen_etal_2018
. This is a promising direction, however, its application is limited by the fact that it typically uses knowledge of the task presentation schedule to update the loss function after each switch in the data distribution.
Most of the continual learning literature, including all of the approaches discussed above, have been developed in task-based settings, where representations are learnt implicitly. While deep networks learn well in such settings achille2017emergence ; shwartz2017opening , this often comes at a cost of reduced positive transfer. This is because the implicitly learnt representations often overfit to the training task by discarding information that is irrelevant to the current task but may be required for solving future tasks achille2017emergence ; achille2018information ; achille2017separation ; shwartz2017opening ; Higgins_etal_2017bachille2018information ; alemi2016deep ; Higgins_etal_2017 has demonstrated the usefulness of information-theoretic methods in such settings. These approaches can broadly be seen as efficient implementations of the Minimum Description Length (MDL) principle for unsupervised learning rissanen1978modeling ; grunwald2007minimum
. The representations learnt through such methods have been shown to help in transfer scenarios and with data efficiency for policy learning in the Reinforcement Learning (RL) contextHiggins_etal_2017b . These approaches, however, do not immediately generalise to non-stationary data. Indeed, life-long unsupervised representation learning is relatively under-developed Shin_etal_2017 ; Seff_etal_2017 ; Nguyen_etal_2018 . The majority of recent work in this direction has concentrated on implicit generative models Shin_etal_2017 ; Seff_etal_2017 , or non-parametric approaches Milan_etal_2016 . Since these approaches do not possess an inference mechanism, they are unlikely to be useful for subsequent task or policy learning. Furthermore, none of the existing approaches explicitly investigate meaningful sharing of latent representations between environments.
3.1 Problem formalisation
We assume that there is an a priori unknown set of environments which, between them, share a set of independent data generative factors. We assume . Since we aim to model piece-wise stationary data, it is reasonable to assume , where
is the probability of observing environment. Two environments may use the same generative factors but render them differently, or they may use a different subset of factors altogether. Given an environment , and an environment-dependent subset of the ground truth generative factors, it is possible to synthesise a dataset of images . In order to keep track of which subset of the data generative factors is used by each environment to generate images , we introduce an environment-dependent mask with dimensionality , where if and zero otherwise. Hence, we assume , where is the probability that factor is used in environment . This leads to the following generative process (where “” is element-wise multiplication):
Intuitively, we assume that the piece-wise stationary observed data can be split into clusters (environments ) (note evidence for similar experience clustering from the animal literature auchter2017reconsolidation ). Each cluster has a set of standard coordinate axes (a subset of the generative factors chosen by the latent mask ) that can be used to parametrise the data in that cluster (fig. 1A). Given a sequence of datasets generated according to the process in eq. 1, where is the
-th sample of the environment, the aim of life-long representation learning can be seen as estimating the full set of generative factorsfrom the environment-specific subsets of inferred on each stationary data cluster . Henceforth, we will drop the subscript for simplicity of notation.
3.2 Inferring the data generative factors
Observations cannot contain information about the generative factors that are not relevant for the environment . Hence, we use the following form for representing the data generative factors:
Note that and in eq. 2 depend only on the data and not on the environment . This is important to ensure that the semantic meaning of each latent dimension remains consistent for different environments . We model the representation
of the data generative factors as a product of independent normal distributions to match the assumed prior.
In order to encourage the representation to be semantically meaningful, we encourage it to capture the generative factors of variation within the data by following the MDL principle. We aim to find a representation that minimises the reconstruction error of the input data conditioned on under a constraint on the quantity of information in . This leads to the following loss function:
The loss in eq. 3 is closely related to the -VAE Higgins_etal_2017 objective , which uses a Lagrangian to limit the latent bottleneck capacity, rather than an explicit target . It was shown that optimising the -VAE objective helps with learning a more semantically meaningful disentangled representation of the data generative factors Higgins_etal_2017 . However, Burgess_etal_2017 showed that progressively increasing the target capacity in eq. 3 throughout training further improves the disentanglement results reported in Higgins_etal_2017 , while simultaneously producing sharper reconstructions. Progressive increase of the representational capacity also seems intuitively better suited to continual learning where new information is introduced in a sequential manner. Hence, VASE optimises the objective function in eq. 3 over a sequence of datasets . This, however, requires a way to infer and , as discussed next.
3.3 Inferring the latent mask
Given a dataset , we want to infer which latent dimensions were used in its generative process (see eq. 1). This serves multiple purposes: 1) helps identify the environment (see next section); 2) helps ignore latent factors that encode useful information in some environment but are not used in the current environment , in order to prevent retraining and subsequent catastrophic forgetting; and 3) promotes latent sharing between environments. Remember that eq. 3 indirectly optimises for after training on a dataset . If a new dataset uses the same generative factors as , then the marginal behaviour of the corresponding latent dimensions will not change. On the other hand, if a latent dimension encodes a data generative factor that is irrelevant to the new dataset, then it will start behaving atypically and stray away from the prior. We capture this intuition by defining the atypicality score for each latent dimension on a batch of data :
The atypical components are unlikely to be relevant to the current environment, so we mask them out:
is a threshold hyperparameter (seesections A.3 and A.2 for more details). Note that the uninformative latent dimensions that have not yet learnt to represent any data generative factors, i.e. , are automatically unmasked in this setup. This allows them to be available as spare latent capacity to learn new generative factors when exposed to a new dataset. Fig. 2 shows the sharp changes in at dataset boundaries during training.
3.4 Inferring the environment
Given the generative process introduced in eq. 1, it may be tempting to treat the environment as a discrete latent variable and learn it through amortised variational inference. However, we found that in the continual learning scenario this is not a viable strategy. Parametric learning is slow, yet we have to infer each new data cluster extremely fast to avoid catastrophic forgetting. Hence, we opt for a fast non-parametric meta-algorithm motivated by the following intuition. Having already experienced datasets during life-long learning, there are two choices when it comes to inferring the current one : it is either a new dataset , or it is one of the datasets encountered in the past. Intuitively, one way to check for the former is to see whether the current data seems likely under any of the previously seen environments. This condition on its own is not sufficient though. It is possible that environment uses a subset of the generative factors used by another environment , in which case environment will explain the data well, yet it will be an incorrect inference. Hence, we have to ensure that the subset of the relevant generative factors inferred for the current data according to section 3.3 matches that of the candidate past dataset . Given a batch , we infer the environment according to:
is the output of an auxiliary classifier trained to infer the most likely previously experienced environmentgiven the current batch , is the average reconstruction error observed for the environment when it was last experienced, and is a threshold hyperparameter (see section A.2 for details).
3.5 Preventing catastrophic forgetting
So far we have discussed how VASE integrates knowledge from the current environment into its representation , but we haven’t yet discussed how we ensure that past knowledge is not forgotten in the process. Most standard approaches to preventing catastrophic forgetting discussed in section 2 are either not applicable to a variational context, or do not scale well due to memory requirements. However, thanks to learning a generative model of the observed environments, we can prevent catastrophic forgetting by periodically hallucinating (i.e. generating samples) from past environments using a snapshot of VASE, and making sure that the current version of VASE is still able to model these samples. A similar “dreaming” feedback loop was used in ramapuram2017lifelong ; Shin_etal_2017 ; Seff_etal_2017 ; Ans_Rousset_1997 .
More formally, we follow the generative process in eq. 1 to create a batch of samples using a snapshot of VASE with parameters (see fig. 1C). We then update the current version of VASE according to the following (replacing with for brevity):
where is a distance between two distributions (we use the Wasserstein distance for the encoder and KL divergence for the decoder). The snapshot parameters get synced to the current trainable parameters , every training steps, where is a hyperparameter. The expectation over simulators and latents in eq. 7 is done using Monte Carlo sampling (see section A.2 for details).
3.6 Model summary
To summarise, we train our model using a meta-algorithm with both parametric and non-parametric components. The latter is needed to quickly associate new experiences to an appropriate cluster, so that learning can happen inside the current experience cluster, without disrupting unrelated clusters. We initialise the latent representation to have at least as many dimensions as the total number of the data generative factors
, and the softmax layer of the auxiliary environment classifier to be at least as large as the number of datasets. As we observe the sequence of training data, we detect changes in the environment and dynamically update the internal estimate of datasets experienced so far according to eq. 6. We then train VASE by minimising the following objective function:
Continual learning with disentangled shared latents
First, we qualitatively assess whether VASE is able to learn good representations in a continual learning setup. We use a sequence of three datasets: (1) a moving version of Fashion-MNIST Xiao_etal_2017 (shortened to moving Fashion), (2) MNIST LeCun_etal_1998 , and (3) a moving version of MNIST (moving MNIST). During training we expect VASE to detect shifts in the data distribution and dynamically create new experience clusters , learn a disentangled representation of each environment without forgetting past environments, and share disentangled factors between environments in a semantically meaningful way. Fig. 2 (top) compares the performance of VASE to that of Controlled Capacity Increase-VAE (CCI-VAE) Burgess_etal_2017 , a model for disentangled representation learning with the same architecture as VASE but without the modifications introduced in this paper to allow for continual learning. It can be seen that unlike VASE, CCI-VAE forgot moving Fashion at the end of the training sequence. Both models were able to disentangle position from object identity, however, only VASE was able to meaningfully share latents between the different datasets - the two positional latents are active for two moving datasets but not for the static MNIST. VASE also has moving Fashion- and MNIST-specific latents, while CCI-VAE shares all latents between all datasets. VASE use only 8/24 latent dimensions at the end of training. The rest remained as spare capacity for learning on future datasets.
Learning representations for tasks
We train object identity classifiers (one each for moving Fashion and MNIST) and an object position regressor on top of the latent representation at regular intervals throughout the continual learning sequence. Good accuracy on these measures would indicate that at the point of measurement, the latent representation contained dataset relevant information, and hence could be useful, e.g. for subsequent policy learning in RL agents. Figure 2 (bottom) shows that both VASE and CCI-VAE learn progressively more informative latent representations when exposed to each dataset , as evidenced by the increasing classification accuracy and decreasing mean squared error (MSE) measures within each stage of training. However, with CCI-VAE, the accuracy and MSE measures degrade sharply once a domain shift occurs. This is not the case for VASE, which retains a relatively stable representation.
Here we perform a full ablation study to test the importance of the proposed components for unsupervised life-long representation learning: 1) regularisation towards disentangled representations (section 3.2), 2) latent masking (section 3.3 - A), 3) environment clustering (section 3.4 - S), and 4) “dreaming” feedback loop (section 3.5 - D). We use the constraint capacity loss in eq. 3 for the disentangled experiments, and the standard VAE loss Kingma_Welling_2014 ; Rezende_etal_2014 for the entangled experiments Higgins_etal_2017 . For each condition we report the average change in the classification metrics reported above, and the average maximum values achieved (see section A.5 for details). Table 1 shows that the unablated VASE (SDA) has the best performance. Note that the entangled baselines perform worse than the disentangled equivalents, and that the capacity constraint of the CCI-VAE framework does not significantly affect the maximal classification accuracy compared to the VAE. It is also worth noting that VASE outperforms the entangled SD condition, which is similar to the only other baseline VAE-base approach to continual learning that we are aware of ramapuram2017lifelong . We have also trained VASE on longer sequences of datasets (moving MNIST Fashion inverse Fashion MNIST moving Fashion) and found similar levels of performance (see fig. 3).
|Object ID Accuracy||Position MSE||Object ID Accuracy||Position MSE|
|Ablation||Max (%)||Change (%)||Min (*1e-4)||Change (*1e-4)||Max (%)||Change (%)||Min (*1e-4)||Change (*1e-4)|
|-||88.6 (0.4)||-15.2 (2.8)||3.5 (0.05)||24.8 (13.5)||91.8 (0.4)||-12.1 (0.8)||4.2 (0.7)||10.5 (2.6)|
|S||88.9 (0.5)||-13.9 (1.9)||3.4 (0.05)||22.5 (12.2)||91.7 (0.4)||-12.2 (0.03)||4.5 (0.8)||10.9 (3.1)|
|D||88.6 (0.3)||-14.4 (1.9)||3.3 (0.04)||21.4 (4.9)||91.8 (0.4)||-12.4 (0.7)||4.3 (0.7)||11.7 (3.2)|
|A||86.7 (1.9)||-24.5 (1.0)||3.3 (0.04)||67.6 (107.0)||88.6 (0.3)||-19.7 (0.5)||4.5 (0.7)||47.1 (26.2)|
|SA||87.1 (1.8)||-28.1 (0.08)||3.3 (0.04)||78.9 (109.0)||89.9 (1.3)||-18.3 (0.4)||4.8 (0.7)||41.8 (20.6)|
|DA||86.3 (2.5)||-25.2 (0.5)||3.3 (0.04)||72.2 (90.0)||88.8 (0.3)||-19.4 (0.4)||4.6 (0.7)||40.2 (19.2)|
|SD||88.3 (0.3)||-12.9 (1.9)||3.4 (0.05)||20.0 (3.5)||91.4 (0.3)||-11.7 (0.6)||4.3 (0.5)||11.6 (1.9)|
|VASE (SDA)||88.6 (0.4)||-5.4 (0.3)||3.2 (0.03)||3.0 (0.2)||91.5 (0.1)||-6.5 (0.7)||4.2 (0.4)||3.9 (1.1)|
Dealing with ambiguity
Natural stimuli are often ambiguous and may be interpreted differently based on contextual clues. Examples of such processes are common, e.g. visual illusions like the Necker cube Necker_1832 , and may be driven by the functional organisation and the heavy top-down influences within the ventral visual stream of the brain Balasz_etal_1993 ; Przybyszewski_1998 . To evaluate the ability of VASE to deal with ambiguous inputs based on the context, we train it on a CelebA Liu_etal_2015
inverse Fashion sequence, and test it using ambiguous linear interpolations between samples from the two datasets (fig. 4A, first row). To measure the effects of ambiguity, we varied the interpolation weights between the two datasets. To measure the effects of context, we presented the ambiguous samples in a batch with real samples from one of the training datasets, varying the relative proportions of the two. Figure 4A (bottom) shows the inferred probability of interpreting the ambiguous samples as CelebA . VASE shows a sharp boundary between interpreting input samples as Fashion or CelebA despite smooth changes in input ambiguity. Such categorical perception is also characteristic of biological intelligence Etcoff_Magee_1992 ; Freedman_etal_2001 ; Liu_Jagadeesh_2008
. The decision boundary for categorical perception is affected by the context in which the ambiguous samples are presented. VASE also represents its uncertainty about the ambiguous inputs by increasing the inferred variance of the relevant latent dimensions (fig. 4A, second row).
Here we test whether VASE can learn more sophisticated cross-domain latent homologies than the positional latents on the moving MNIST and Fashion datasets described above. Hence, we trained VASE on a sequence of two visually challenging DMLab-30 111https://github.com/deepmind/lab/tree/master/game_scripts/levels/contributed/dmlab30#dmlab-30 deepmind_lab datasets: the Exploit Deferred Effects (EDE) environment and a randomized version of the Natural Labyrinth (NatLab) environment (Varying Map Randomized). While being visually very distinct (one being indoors and the other outdoors), the two datasets share many data generative factors that have to do with the 3D geometry of the world (e.g. horizon, walls/terrain, objects/cacti) and the agent’s movements (first person optic flow). Hence, the two domains share many semantically related factors , but these are rendered into very different visuals . We compared cross-domain reconstructions of VASE and an equivalent entangled VAE () baseline. The reconstructions were produced by first inferring a latent representation based on a batch from one domain, e.g. , and then reconstructing them conditioned on the other domain . Fig. 4 shows that VASE discovered the latent homologies between the two domains, while the entangled baseline failed to do so. VASE learnt the semantic equivalence between the cacti in NatLab and the red objects in EDE, the brown fog corresponding to the edge of the NatLab world and the walls in EDE (top leftmost reconstruction), and the horizon lines in both domains. The entangled baseline, on the other hand, seemed to rely on the surface-level pixel statistics and hence struggled to produce meaningful cross-domain reconstructions, attempting to match the texture rather than the semantics of the other domain. See section A.6 for additional cross-domain reconstructions, including on the sequence of five datasets mentioned earlier.
Once we learn the concept of moving objects in one environment, it is reasonable to imagine that a novel object encountered in a different environment can also be moved. Given the ability to act, we may try to move the object to realise our hypothesis. We can use such imagination-driven exploration to augment our experiences in an environment and let us learn a richer representation. Notice however, that such imagination requires a compositional representation that allows for novel yet sensible recombinations of previously learnt semantic factors. We now investigate whether VASE can use such imagination-driven exploration to learn better representations using a sequence of three datasets: moving Fashion MNIST moving MNIST. During the first moving Fashion stage, VASE learns the concepts of position and Fashion sprites. It also learns how to move the sprites to reach the imagined states by training an auxiliary policy (see section A.3.1 for details). It can then use this policy to do an imagination-based augmentation of the input data on MNIST by imagining MNIST digits in different positions and transforming the static sprites correspondingly using the learnt policy. Hence, VASE can imagine the existence of moving MNIST before actually experiencing it. Indeed, fig. 4C shows that when we train a moving MNIST classifier during the static MNIST training stage, the classifier is able to achieve good accuracy in the imagination-driven exploration condition, highlighting the benefits of imagination-driven data augmentation.
We have introduced VASE, a novel approach to life-long unsupervised representation learning that builds on recent work on disentangled factor learning Higgins_etal_2017 ; Burgess_etal_2017 by introducing several new key components. Unlike other approaches to continual learning, our algorithm does not require us to maintain a replay buffer of past datasets, or to change the loss function after each dataset switch. In fact, it does not require any a priori knowledge of the dataset presentation sequence, since these changes in data distribution are automatically inferred. We have demonstrated that VASE can learn a disentangled representation of a sequence of datasets. It does so without experiencing catastrophic forgetting and by dynamically allocating spare capacity to represent new information. It resolves ambiguity in a manner that is analogous to the categorical perception characteristic of biological intelligence. Most importantly, VASE allows for semantically meaningful sharing of latents between different datasets, which enables it to perform cross-domain inference and imagination-driven exploration. Taken together, these properties make VASE a promising algorithm for learning representations that are conducive to subsequent robust and data-efficient RL policy learning.
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Appendix A Supplemental Details
a.1 Model details
Encoder and decoder
For the encoder we use a simple convolutional network with the following structure:
conv 64 conv 64 conv 128 conv 128 fc 256, where conv n_filters is a convolution with n_filtersfc n_out is a fully connected layer with n_out units. The output of the fully connected layer is given to a linear layer that outputs the mean and log-variance of the encoder posterior . The decoder network receives a sample from the encoder and outputs the parameters of a distribution over . We use the transpose of the encoder network, but we also feed it the environment index by first encoding it with a one-hot encoding (of size . For most of the experiments, we use a product of independent Bernoulli distributions (parametrised by the mean) for the decoder distribution . In the DM Lab experiments we use instead a product of Gaussian distributions with fixed variance. We train the model using Adam
by first encoding it with a one-hot encoding (of sizemax_environments, which is a hyperparameter), and then concatenating it to
. For most of the experiments, we use a product of independent Bernoulli distributions (parametrised by the mean) for the decoder distribution
. In the DM Lab experiments we use instead a product of Gaussian distributions with fixed variance. We train the model using Adam with a fixed learning rate 6e-4 and batch size 64.
Environment inference network
We attach an additional fully connected layer to the last layer of the encoder (gradients to the encoder are stopped). Given an input image , the layer outputs a softmax distribution over max_environments indices, which tries to infer the most likely index of the environment from which the data is coming, assuming the environment was already seen in the past. Notice that we always know the (putative) index of the current data ( in Equation 6, also see Section A.2), so that we can always train this layer to associate the current data to the current index. However, to avoid catastrophic forgetting we also need to train on hallucinated data from past environments. Assuming is the current environment and is the total number of environments seen until now, the resulting loss function is given by:
where the hallucinated data in the second part of the equation is generated according to Section 3.5, and the expectation over is similarly done through Monte Carlo sampling.
a.2 Extra algorithm implementation details
Atypical latent components
The atypicality of the component on a batch of samples is computed using a KL divergence from the marginal posterior over the batch to the prior according to Equation 4. In practice it is not convenient to compute this KL divergence directly. Rather, we observe that the marginal distribution of the latent samples is approximately Gaussian. We exploit this by fitting a Gaussian to the latent samples and then computing in closed-form the KL-divergence between this approximation of the marginal and the unit Gaussian prior .
Recall from Section 3.3 that we deem a latent component to be active () whenever it is typical, that is, if . However, since the atypicality is computed on a relatively small batch of samples, may be a noisy estimate of atypicality. Hence we introduce the following filtering: we set if and if , with . If , we leave unchanged.
Used latent components
We say that a factor is not used by the environment if the reconstruction does not depend on . To measure this, we find the maximum amount of noise we can add to without changing the reconstruction performance of the network. That is, we optimise
where . If for some threshold , we say that is unused. We generally observe that components are either completely unused , or else is very large. Therefore, picking a threshold is very easy and the precise value does not matter. We only compare the atypicality masks in eq. 6 for the used latents.
Expanding on the explanation in Section 3.4, let be the reconstruction loss on a batch of data, assuming it comes from the environment . Let be the average reconstruction loss observed in the past for data from environment . Let be the number of datasets observed until now. Let be a binary vector of used units computed with the method described before.
be a binary vector of used units computed with the method described before.
We run the auxiliary environment inference network (Section A.1) on each sample from the batch and take the average of all results in order to obtain a probability distribution
and take the average of all results in order to obtain a probability distributionover the possible environment of the batch , assuming it has already been seen in the past. Let be the most likely environment, which is our candidate for the new environment. If the reconstruction loss (assuming ) is significantly larger (see Algorithm 1) than the average loss for the environment , we decide that the data is unlikely to come from this environment, and hence we allocate a new one. If the reconstruction is good, but some of the used components (given by ) are atypical, we still allocate a new environment. Otherwise, we assume that the data indeed comes from .
a.3 Hyperparameter sensitivity
Table 2 lists the values of the hyperparameters used in the different experiments reported in this paper.
|Ablation Study (150k)||100.0||35.0||6.3e-6||0.6||1.5||500|
|Five Datasets (150k)||100.0||35.0||6.3e-6||0.6||1.5||5000|
|CelebA Inverted Fashion (30k)||200.0||20.0||1.7e-5||0.8||1.1||500|
|NatLab EDE (60k)||200.0||25.0||1e-5||2.0||1.1||5000||20.0||1.1||5000|
|Imagination-Driven Exploration (45k)||200.0||35.0||0.7e-5||0.7||1.5||500|
For all experiments we use max_environments = 7, and we increase in eq. 3 linearly by per step (starting from 0) until it reaches , at which point we keep fixed at that value. In the loss function eq. 8, the dreaming loss was re-weighted, with the full loss being:
The values and were used for all experiments, except for in the hyperparameter sweep.
For the ablation study we ran a hyperparameter search using the full model, and used the best hyperparameters found for all experiments. We list the search ranges and our observed sensitivity to these hyperparameters next:
= coefficient for the capacity constraint – – found not to be very sensitive.
= final value of – – classification accuracy increased significantly for capacity from 20 to 35.
= atypicality threshold – – lower threshold led to more latent freezing. Classification performance was not very sensitive to this.
= update frequency reference network – – found not to be very sensitive.
= weight for encoder loss in "dreaming" loop – – found not to be very sensitive.
= weight for decoder loss in "dreaming" loop – – found not to be very sensitive.
a.3.1 Imagination-based exploration experiments
We model a very simple interaction with the environment where the agent can translate the observed object (in our case the input image). The agent is trained as follows: a random is sampled from the prior . Given an observation from the environment, the agent needs to pick an action (in our case a translation) in such a way that the encoding of the new image is as close as possible to . That is, we minimise the loss
The agent can then be used to explore the current environment. Given an image from the current environment, we can imagine a configuration of the latent factors, and let the agent act on the environment in order to realise the configuration. In this way, we obtain a new image . We can then add the image to the training data, which allows the encoder to learn from a possibly more diverse set of inputs than the inputs observed passively.
The policy network first processes the input image (of size ) though four convolutional layers with 16 filters, stride 2 and ReLU activations. The resulting vector is concatenated with the target , and feed to a 1-hidden layer fully connected network that outputs the parameters of the 2D translation to apply to the image. We use a tanh output to ensure the translation is always in a sensible range. Once these parameters are obtained, the transformation is applied to the image using a Spatial Transformer Network (STN) , obtaining a translated image . We can now finally compute the resulting representation . Notice that the whole operation is fully differentiable, thanks to the properties of the STN. The policy is can now be trained by minimising in order to make and as close as possible. In our experiments we train the policy while training the main model.
a.4 Dataset processing
We used an IMPALA agent trained on all DM-30 tasks  to generate data. We take observations of this optimal agent (collecting rewards according to the task descriptions explained in https://github.com/deepmind/lab/tree/master/game_scripts/levels/contributed/dmlab30), on randomly generated episodes of Exploit Deferred Effects and NatLab Varying Map Randomized; storing them as RGB tensors.
We crop the right-most 27 pixels out to obtain a
RGB tensors. We crop the right-most 27 pixels out to obtain aimage (this retains the most useful portion of the original view), which are finally scaled down to (using tf.image.resize_area).
CelebA Inverse Fashion
To make CelebA compatible with Fashion, we convert the CelebA images to grayscale and extract a patch of size centered on the face. We also invert the colours of Fashion so that the images are black on a white background, and slightly reducing the contrast, in order to make the two datasets more similar, and hence easier to confuse after mixing.
a.5 Quantifying catastrophic forgetting
We train on top of the representation a simple 2-hidden layers fully connected classifier with 256 hidden units per layer and ReLU activations. At each step while training the representation, we also train a separate classifier on the representation for each environment, using Adam with learning rate 6e-4 and batch size 64. This classifier training step does not update the weights in the main network.
For each ablation type we reported the average classification accuracy (or regression MSE) score obtained by 20 replicas of the model, all with the best set of hyperparameters discovered for the full model. We quantified catastrophic forgetting by reporting the average difference between the maximum accuracy obtained while VASE was training on a particular dataset and the minimum accuracy obtained for the dataset afterwards.
a.6 Additional results
We present additional experimental results and extra plots for the experiments reported in the main paper here. Fig. 5 and table 3 show latent traversals and quantitative evaluation results for an ablation study on VASE trained on the MNIST Fashion MNIST sequence. Fig. 5 also shows traversals for VASE trained on the DM Lab levels NatLab EDE. This is the model reported in section 4. Fig. 6 shows cross-dataset reconstructions for VASE trained on the moving Fashion MNIST moving MNIST sequence described in the ablation study in section 4. Figs. 7-8 shows latent traversals and cross-dataset reconstructions for VASE trained on the moving MNIST Fashion inverted Fashion MNIST moving Fashion sequence described in the main text.
|Configuration||Avg. Decrease (%)||Avg. Max (%)||Avg. Decrease (%)||Avg. Max (%)|