A weakness of neural networks is that they often require a large amount of labeled data to perform well. Although self-supervised/unsupervised representation learning(Hinton et al., 2006; Bengio et al., 2007; Raina et al., 2007; Vincent et al., 2010)
was attempted to address this weakness, most practical neural network systems today are trained with supervised learning (e.g.,(Hannun et al., 2014; He et al., 2016a; Wu et al., 2016)). Making use of unlabeled data through unsupervised representation learning to improve data-efficiency of neural networks remains an open challenge for the field.
Recently, language model pretraining has been suggested as a method for unsupervised representation learning in NLP (Dai and Le, 2015; Ramachandran et al., 2017; Peters et al., 2018; Howard and Ruder, 2018; Devlin et al., 2019). Most notably, Devlin et al. (2019) made an observation that bidirectional representations from input sentences are better than left-to-right or right-to-left representations alone. Based on this observation, they proposed the concept of masked language modeling by masking out words in a context to learn representations for text, also known as BERT. This is crucially achieved by replacing the LSTM architecture with the Transformer feedforward architecture (Vaswani et al., 2017). The feedforward nature of the architecture makes BERT more ready to be applied to images. Yet BERT still cannot be used for images because images are continuous objects unlike discrete words in sentences. We hypothesize that bridging this last gap is key to translating the success of language model pretraining to the image domain.
In this paper, we propose a pretraining method called Selfie, which stands for SELF-supervised Image Embedding. Selfie generalizes BERT to continuous spaces, such as images. In Selfie
, we propose to continue to use classification loss because it is less sensitive to small changes in the image (such as translation of an edge) compared to regression loss which is more sensitive to small perturbations. Similar to BERT, we mask out a few patches in an image and try to reconstruct the original image. To enable the classification loss, we sample “distractor” patches from the same image, and ask the model to classify the right patch to fill in a target masked location.
Experiments show that Selfie works well across many datasets, especially when the datasets have a small number of labeled examples. On CIFAR-10, ImagetNet , and ImageNet , we observe consistent accuracy gains as we vary the amount of labeled data from 5% to 100% of the typical training sets. The gain tends to be biggest when the labeled set is small. For example, on ImageNet with only 60 labeled examples per class, pretraining using our method improves the mean accuracy of ResNet-50 by 11.1%, going from 35.6% to 46.7%. Additional analysis on ImageNet provides evidence that the benefit of self-supervised pretraining significantly takes off when there is at least an order of magnitude (10X) more unlabeled data than labeled data.
In addition to improving the averaged accuracy, pretraining ResNet-50 on unlabeled data also stabilizes its training on the supervised task. We observe this by computing the standard deviation of the final test accuracy across 5 different runs for all experiments. On CIFAR-10 with 400 examples per class, the standard deviation of the final accuracy reduces 3 times comparing to training with the original initialization method. Similarly, on ImageNet rescaled to , our pretraining process gives an 8X reduction on the test accuracy variability when training on 5% of the full training set.
An overview of Selfie is shown in Figure 1. Similar to previous works in unsupervised/self-supervised representation learning, our method also has two stages: (1) Pretrain the model on unlabeled data and then (2) Finetune on the target supervised task. To make it easy to understand, let us first focus on the fine-tuning stage. In this paper, our goal is to improve ResNet-50, so we will pretrain the first three blocks of this architecture.111In our experiments, we found using the first three convolution blocks gives similar results to the full network (4 convolution blocks). During pretraining, therefore, only the first three blocks (i.e. ResNet-36) are used to save computation and memory load. Let us call this network . The pretraining stage is therefore created for training this network in an unsupervised fashion.
Now let us focus on the pretraining stage. In the pretraining stage, , a patch processing network, will be applied to small patches in an image to produce one feature vector per patch for both the encoder and the decoder. In the encoder, the feature vectors are pooled together by an attention pooling network to produce a single vector . In the decoder, no pooling takes place; instead the feature vectors are sent directly to the computation loss to form an unsupervised classification task. The representations from the encoder and decoder networks are jointly trained during pretraining to predict what patch is being masked out at a particular location among other distracting patches.
In our implementation, to make sure the distracting patches are hard, we sample them from the same input image and also mask them out in the input image. Next we will describe in detail the interaction between the encoder and decoder networks during pretraining as well as different design choices.
2.1 Pretraining Details
The main idea is to use a part of the input image to predict the rest of the image during this phase. To do so, we first sample different square patches from the input. These patches are then routed into the encoder and decoder networks depending on whether they are randomized to be masked out or not. Let us take Figure 1 as an example, where are sent into the encoder, whereas are sent into the decoder.
All the patches are processed by the same patch processing network . On the encoder side, the output vectors produced by are routed into the attention pooling network to summarize these representations into a single vector . On the decoder side, creates output vectors , , . The decoder then queries the encoder by adding to the output vector the location embedding of a patch, selected at random among the patches in the decoder (e.g., ) to create a vector . The vector is then used in a dot product to compute the similarity between and each . Having seen the dot products between and ’s, the decoder has to decide which patch is most relevant to fill in the chosen location (at ). The cross entropy loss is applied for this classification task, whereas the encoder and decoder are trained jointly with gradients back-propagated from this loss.
During this pretraining process, the encoder network learns to compress the information in the input image to a vector such that when seeded by a location of a missing patch, it can recover that patch accurately. To perform this task successfully, the network needs to understand the global content of the full image, as well as the local content of each individual patch and their relative relationship. This ability proves to be useful in the downstream task of recognizing and classifying objects.
Patch sampling method.
On small images of size , we use a patch size of , while on larger images of size , we use a patch size of . The patch size is intentionally selected to divide the image evenly, so that the image can be cut into a grid as illustrated in Figure 1
. To add more randomness to the position of the image patches, we perform zero padding of 4 pixels on images with sizeand then random crop the image to its original size.
Patch processing network.
In this work, we focus on improving ResNet-50 (He et al., 2016a) on various benchmarks by pretraining it on unlabeled data. For this reason, we use ResNet-50 as the patch processing network .222Our implementation of ResNet-50 achieves 76.9 0.2 top-1 accuracy on ImageNet, which is in line with other results reported in the literature (He et al., 2016a; Zagoruyko and Komodakis, 2016; Huang et al., 2017). As described before, only the first three blocks of ResNet-50 is used. Since the goal of is to reduce any image patch into a single feature vector, we therefore perform average pooling across the spatial dimensions of the output of ResNet-36.
Efficient implementation of mask prediction.
For a more efficient use of computation, the decoder is implemented to predict multiple correct patches for multiple locations at the same time. For example, in the example above, besides finding the right patch for , the decoder also tries to find the right patch for as well as . This way, we reuse three times as much computation from the encoder-decoder architecture. Our method is, therefore, analogous to solving a jigsaw puzzle where a few patches are knocked out from the image and are required to be put back to their original locations. This procedure is demonstrated in Figure 2.
2.2 Attention Pooling
In this section, we describe in detail the attention pooling network introduced in Section 2.1 and the way positional embeddings are built for images in our work.
Transformer as pooling operation.
We make use of Transformer layers to perform pooling. Given a set of input vectors produced by applying the patch processing network on different patches, we want to pool them into a single vector
to represent the entire image. There are multiple choices at this stage including max pooling or average pooling. Here, we consider these choices special cases of the attention operation (where the softmax has a temperature approaching zero or infinity respectively) and let the network learn to pool by itself. To do this, we learn a vectorwith the same dimension with ’s and feed them together through the Transformer layers:
The output corresponding to input is the pooling result. We discard .
Each self-attention block follows the design in BERT (Devlin et al., 2019) where self-attention layer is followed with two fully connected layers that sequentially project the input vector to an intermediate size and back to the original hidden size. The only non-linearity used is GeLU and is applied at the intermediate layer. We perform dropout with rate
on the output, followed by a residual connection connecting from the block’s input and finally layer normalization.
For images of size , we learn a positional embedding vector for each of the 16 patches of size . Images of size , on the other hand, are divided into a grid of patches of size . Since there are significantly more positions in this case, we decompose each positional embedding into two different components: row and column embeddings. The resulting embedding is the sum of these two components. For example, instead of learning 49 positional embeddings, we only need to learn positional embeddings. This greatly reduces the number of parameters and helps with regularizing the model.
2.3 Finetuning Details
As mentioned above, in this phase, the first three convolution blocks of ResNet-50 is initialized from the pretrained patch processing network. The last convolution block of ResNet-50 is initialized by the standard initialization method. ResNet-50 is then applied on the full image and finetuned end-to-end.
3 Experiments and Results
In the following sections, we investigate the performance of our proposed pretraining method, Selfie, on standard image datasets, such as CIFAR-10 and ImageNet. To simulate the scenario when we have much more unlabeled data than labeled data, we sample small fractions of these datasets and use them as labeled datasets, while the whole dataset is used as unlabeled data for the pretraining task.
We consider three different datasets: CIFAR-10, ImageNet resized to , and ImageNet original size (). For each of these datasets, we simulate a scenario where an additional amount of unlabeled data is available besides the labeled data used for the original supervised task. For that purpose, we create four different subsets of the supervised training data with approximately 5%, 10%, 20%, and 100% of the total number of training examples. On CIFAR-10, we replace the 10% subset with one of 4000 training examples (8%), as this setting is used in (Oliver et al., 2018; Cubuk et al., 2018). In all cases, the whole training set is used for pretraining (50K images for CIFAR-10, and 1.2M images for ImageNet).
3.2 Experimental setup
We reuse all settings for ResNet convolution blocks from ResNet-50v2 including hidden sizes and initialization (He et al., 2016b)
. Batch normalization is performed at the beginning of each residual block. For self-attention layers, we apply dropout on the attention weights and before each residual connection with a drop rate of 10%. The sizes of all of our models are chosen such that each architecture has roughly 25M parameters and 50 layers, the same size and depth of a standard ResNet-50. For attention pooling, three attention blocks are added with a hidden size of, intermediate size and attention heads on top of the patch processing network .
Both pretraining and finetuning tasks are trained using Momentum Optimizer with Nesterov coefficient of . We use a batch size of for CIFAR-10 and for ImageNet. Learning rate is scheduled to decay in a cosine shape with a warm up phase of 100 steps and the maximum learning rate is tuned in the range of . We do not use any extra regularization besides an weight decay of magnitude . The full training is done in steps. Furthermore, as described in Section 2.1, we divide the images into non-overlapping square patches of size or during pretraining and sample a fraction of these patches to predict the remaining. We try for two values of : 75% or 50% and tune it as a hyper-parameter.
For each reported experiment, we first tune its hyper-parameters by using 10% of training data as validation set and train the neural net on the remaining 90%. Once we obtain the best hyper-parameter setting, the neural network is retrained on 100% training data 5 times with different random seeds. We report the mean and standard deviation values of these five runs.
We report the accuracies with and without pretraining across different labeled dataset sizes in Table 1. As can be seen from the table, Selfie yields consistent improvements in test accuracy across all three benchmarks (CIFAR-10, ImageNet , ImageNet ) with varying amounts of labeled data. Notably, on ImageNet , a gain of 11.1% in absolute accuracy is achieved when we use only 5% of the labeled data. We find the pretrained models usually converge to a higher training loss, but generalizes significantly better than model with random initialization on test set. This highlights the strong effect of regularization of our proposed pretraining procedure. An example is shown in Figure 3 when training on 10% subset of Imagenet.
Beside the gain in mean accuracy, training stability is also enhanced as evidenced by the reduction in standard deviation in almost all experiments. When the unlabeled dataset is the same with the labeled dataset (Labeled Data Percentage = 100%), the gain becomes small as expected.
|Labeled Data Percentage|
|CIFAR-10||Supervised||75.9 0.7||79.3 1.0||88.3 0.3||95.5 0.2|
|Selfie Pretrained||75.9 0.4||80.3 0.3||89.1 0.5||95.7 0.1|
|ImageNet||Supervised||13.1 0.8||25.9 0.5||32.7 0.4||55.7 0.6|
|Selfie Pretrained||18.3 0.1||30.2 0.5||33.5 0.2||56.4 0.6|
|ImageNet||Supervised||35.6 0.7||59.6 0.2||65.7 0.2||76.9 0.2|
|Selfie Pretrained||46.7 0.4||61.9 0.2||67.1 0.2||77.0 0.1|
We want to emphasize that our ResNet baselines are very strong compared to those in (He et al., 2016a). Particularly, on CIFAR-10, our ResNet with pure supervised learning on 100% labeled data achieves 95.5% in accuracy, which is better than the accuracy 94.8% achieved by DenseNet (Huang et al., 2017) and close to 95.6% obtained by Wide-ResNet (Zagoruyko and Komodakis, 2016). Likewise, on ImageNet , our baseline reaches 76.9% in accuracy, which is on par with the result reported in (He et al., 2016a), and surpasses the 76.2% accuracy of DenseNet (Huang et al., 2017). Our pretrained models further improve on our strong baselines.
Contrast to Other Works.
Notice that our classification accuracy of 77.0% on ImageNet is also significantly better than previously reported results in unsupervised representation learning (Pathak et al., 2016; Oord et al., 2018; Kolesnikov et al., 2019). For example, in a comprehensive study by (Kolesnikov et al., 2019)
, the best accuracy on ImageNet of all pretraining methods is around 55.2%, which is well below the accuracy of our models. Similarly, the best accuracy reported by Context Autoencoders(Pathak et al., 2016) and Contrastive Predictive Coding (Oord et al., 2018)
are 56.5% and 48.7% respectively. We suspect that such poor performance is perhaps due to the fact that past works did not finetune into the representations learned by unsupervised learning.
Concurrent to our work, there are also other attempts at using unlabeled data in semi-supervised learning settings.Hénaff et al. (2019) showed the effectiveness of pretraining in low-data regime using cross-entropy loss with negative samples similar to our loss. However, their results are not comparable to ours because they employed a much larger network, ResNet-171, compared to the ResNet-50 architecture that we use through out this work. Consistency training with label propagation has also achieved remarkable results. For example, the recent Unsupervised Data Augmentation (Xie et al., 2019) reported 94.7% accuracy on the 8% subset of CIFAR-10. We expect that ur self-supervised pretraining method can be combined with label propagation to provide additional gains, as shown in (Zhai et al., 2019).
Finetuning on ResNet-36 + attention pooling.
In the previous experiments, we finetune ResNet-50, which is essentially ResNet-36 and one convolution block on top, dropping the attention pooling network used in pretraining. We also explore finetuning on ResNet-36 + attention pooling and find that it slightly outperforms finetuning on ResNet-50 in some cases.333We chose to use ResNet-50 for finetuning as it is faster and facilitates better comparison with past works. More in Section 4.2.
Finetuning Sensitivity and Mismatch to Pretraining.
Despite the encouraging results, we found that there are difficulties in transferring pretrained models across tasks such as from ImageNet to CIFAR. For the 100% subset of Imagenet , additional tuning of the pretraining phase using a development set is needed to achieve the result reported in Table 1. There is also a slight mismatch between our pretraining and finetuning settings: during pretraining, we process image patches independently whereas for finetuning, the model sees an image as a whole. We hope to address these concerns in subsequent works.
4.1 Pretraining benefits more when there is less labeled data
In this section, we conduct further experiments to better understand our method, Selfie, especially how it performs as we decrease the amount of labeled data. To do so, we evaluate test accuracy when finetuning on 2%, 5%, 10%, 20% and 100% subset of ImageNet , as well as the accuracy with purely supervised training at each of the five marks. Similar to previous sections, we average results across five different runs for a more stable assessment. As shown in Figure 4, the ResNet mean accuracy improves drastically when there is at least an order of magnitude more unlabeled image than the labeled set (i.e., finetuning on the 10% subset). With less unlabeled data, the gain quickly diminishes. At the 20% mark there is still a slight improvement of 1.4% mean accuracy, while at the 100% mark the positive gain becomes minimal, 0.1%.
4.2 Self-attention as the last layer helps finetuning performance.
As mentioned in Section 3.3, we explore training ResNet-36 + attention pooling (both are reused from pretraining phase) on CIFAR-10 and ImageNet on two settings: limited labeled data and full access to the labeled set. The architectures of the two networks are shown in Figure 5. Experimental results on these two architectures with and without pretraining are reported in Table 2.
|Method||ResNet-50||ResNet-36 + attention pooling|
|CIFAR-10 8%||80.3 0.3||81.3 0.1||+1.0|
|ImageNet 10%||61.8 0.2||62.1 0.2||+0.3|
|CIFAR-10 100%||95.7 0.1||95.4 0.2||-0.3|
|ImageNet 100%||77.0 0.1||77.5 0.1||+0.5|
With pretraining on unlabeled data, ResNet-36 + attention pooling outperforms ResNet-50 on both datasets with limited data. On the full training set, this hybrid convolution-attention architecture gives 0.5% gain on ImageNet . These show great promise for this hybrid architecture which we plan to further explore in future work.
5 Related Work
Unsupervised representation learning for text.
Much of the success in unsupervised representation learning is in NLP. First, using language models to learn embeddings for words is commonplace in many NLP applications (Mikolov et al., 2013; Pennington et al., 2014). Building on this success, similar methods are then proposed for sentence and paragraph representations (Le and Mikolov, 2014; Kiros et al., 2015). Recent successful methods however focus on the use of language models or “masked” language models as pretraining objectives (Dai and Le, 2015; Ramachandran et al., 2017; Peters et al., 2018; Howard and Ruder, 2018; Devlin et al., 2019). A general principle to all of these successful methods is the idea of context prediction: given some adjacent data and their locations, predict the missing words.
Unsupervised representation learning for images.
Recent successful methods in unsupervised representation learning for images can be divided into four categories: 1) predicting rotation angle from an original image (e.g., (Gidaris et al., 2018)), 2) predicting if a perturbed image belongs to the same category with an unperturbed image (Exemplar) (e.g., (Dosovitskiy et al., 2016)), 3) predicting relative locations of patches (e.g., (Doersch et al., 2015)), solving Jigsaw puzzles (e.g., (Noroozi and Favaro, 2016)) and 4) impainting (e.g., (Huang et al., 2014; Pathak et al., 2016; Iizuka et al., 2017)). Their success, however, is limited to small datasets or small settings, some resort to expensive jointing training to surpass their purely supervised counterpart. On the challenging benchmark ImageNet, our method is the first to report gain with and without additional unlabeled data as shown in Table 1.
is also closely related to denoising autoencoders(Vincent et al., 2010), where various kinds of noise are applied to the input and the model is required to reconstruct the clean input. The main difference between our method and denoising autoencoders is how the reconstruction step is done: our method focuses only on the missing patches, and tries to select the right patch among other distracting patches. Our method is also related to Contrastive Predictive Coding (Oord et al., 2018), where negative sampling was also used to classify continuous objects.
Semi-supervised learning is another branch of representation learning methods that take advantage of the existence of labeled data. Unlike pure unsupervised representation learning, semi-supervised learning does not need a separate fine-tuning stage to improve accuracy, which is more common in unsupervised representation learning. Successful recent semi-supervised learning methods for deep learning are based on consistency training(Miyato et al., 2018; Sajjadi et al., 2016; Laine and Aila, 2016; Verma et al., 2019; Xie et al., 2019).
We introduce Selfie, a self-supervised pretraining technique that generalizes the concept of masked language modeling to continuous data, such as images. Given a masked-out position of a square patch in the input image, our method learns to select the target masked patches from negative samples obtained from the same image. This classification objective therefore sidesteps the need for predicting the exact pixel values of the target patches. Experiments show that Selfie achieves significant gains when labeled set is small compared to the unlabeled set. Besides the gain in mean accuracy across different runs, the standard deviation of results is also reduced thanks to a better initialization from our pretraining method. Our analysis demonstrates the revived potential of unsupervised pretraining over supervised learning and that a hybrid convolution-attention architecture shows promise.
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