Video analytics solutions typically use a DNN with pre-trained weights for inference, assuming consistent probability distribution between the training and test dataset. Unfortunately, the inputs to DNN inference might have various distortions that alter the probability distribution and harm DNN performance in practice. An autonomous vehicle may drive in and out of shades, causing abrupt brightness change in the captured video; a drone may change a compression ratio of video frames while streaming to the inference server based on wireless link bandwidth; and edge servers may need to process data from IoT devices with heterogeneous camera hardware and compression strategies. In these scenarios of input distortions with high dynamic range, existing solutions that rely on a DNN with constant pre-trained weights suffer from severe accuracy loss . We observe up to a accuracy loss in our experiments (§5).
One workaround to handle input with unstable distortion level is to train a DNN for each possible distortion level by augmenting the training data to match that particular distortion level, and then switch between them following the distortion level of the current input. However, there are enormous distortion levels (e.g., JPEG has 100 quality levels), running so many DNNs concurrently is infeasible, due to limited memory. Swapping DNNs between disk and memory causes huge latency, and thus, is impractical.
This paper proposes GearNN, an adaptive DNN inference architecture to accommodate real-world inputs with various distortions, without sacrificing memory efficiency. GearNN only adapts a tiny portion (e.g., of the DNN size in §4) of the DNN weights that are “distortion sensitive” (called adaptor) to fit the distortion level of the instantaneous input, while reusing the majority of weights (called base) across all inputs. In this way, the adaptation leads to high inference accuracy, while reusing most weights guarantees the memory efficiency and scalability. We name our design GearNN: like the gearbox that helps a single engine handle different car speeds, GearNN helps a single DNN base to accommodate a wide range of input distortions.
The GearNN workflow can be summarized as follows:
(i) Identifying distortion-sensitive weights offline. Given a DNN pre-trained with undistorted training dataset, GearNN first fine-tunes this DNN to multiple versions, each with training data of a particular distortion level. Next, by comparing the original DNN and the fine-tuned versions, GearNN runs an optimization problem (§3.2.2) to identify a set of distortion-sensitive DNN weights, under a constraint of additional memory budget.
(ii) Partial DNN fine-tuning offline. GearNN then partially fine-tunes the DNN for each pre-defined distortion level, by only updating the distortion-sensitive weights (i.e., adaptor), while freezing the rest of the pre-trained DNN weights (i.e., base). This step yields multiple adaptors, each for a particular distortion level.
(iii) Partial DNN adaptation online. With multiple fine-tuned small adaptors and a single copy of the base loaded in memory, GearNN switches between the adaptors, following a current input distortion level (like compression level), while reusing the base across all possible inputs.
We have prototyped GearNN on top of popular DNN models like DRN [22, 21] and Mask R-CNN  using PyTorch, and performed an extensive evaluation with semantic segmentation and detection tasks (§5). Our evaluation shows that GearNN enables robust DNN inference under various input distortion levels, while retaining memory efficiency. Specifically, GearNN achieves up to higher average inference accuracy over the original DNN trained with undistorted data. GearNN also outperforms other alternatives such as stability training from Google or fine-tuning a DNN using a dataset with mixed distortion levels, and it consumes only more memory over such single DNN solutions to accommodate distortion levels. Meanwhile, compared to switching between multiple DNNs, GearNN reduces memory consumption by , while achieving a similar level of accuracy.
Our contributions can be summarized as follows:
We propose GearNN, a general technique to improve the tolerance of DNN models to input distortions with high dynamic range, without compromising the memory efficiency. We validate GearNN over state-of-the-art DNNs, where it outperforms existing solutions.
GearNN formulates an optimization problem that selects a tiny set of DNN weights to be adapted, under a given constraint of memory consumption.
GearNN is the first to enable robust video analytics on adaptive bitrate (ABR) streaming, following the trend of modern video streaming.
can quickly customize (only takes 4 training epochs in our prototype) any pre-trained DNN to fit the desired dynamic range of input distortion, offering flexibility in deployment.
2 Related Work
discusses adapting the neural network architecture based on the instantaneous input. However, these solutions focus only on accelerating the inference by early exit or skipping convolution layers . In contrast, GearNN aims to improve the DNN robustness under diverse & dynamic input distortions without compromising memory consumption. Besides, GearNN adapts DNN weight values, instead of network architecture, which guarantees fast adaptation and backward compatibility.
Improve training for better robustness. Some existing work proposes to improve the training phase to make DNNs more robust to small input perturbations. Stability training  from Google uses a modified training architecture that flattens the input-output mapping in a small neighborhood of the input image. However, for perturbation with a broad range, it is difficult to flatten one input-output mapping, as shown in Fig. 2. Hence, stability training fails to tolerate various input distortions. On the other hand, GearNN processes the input with different distortion levels by using different input-output mappings, and thus, it is more tolerable under various input distortions.
identifies and removes trivial neurons in order to reduce the model size and computational overhead. This approach looks similar to our solution at first glance, but has fundamental differences.GearNN focuses on the “distortion-sensitive” weights rather than the neurons that are important to the output. Some DNN weights can be insensitive to the input distortion, but can be vital to the output. In other words, GearNN can reuse such weights across different input distortion levels, but the DNN cannot work correctly after pruning such weights. Besides, DNN pruning uses a pruned constant DNN for inference, while GearNN adapts (part of) DNN online during the inference.
Due to the high cost to build a large training dataset, transfer learning[12, 16, 20] is proposed to customize a pre-trained model on one task to fit another task. Although GearNN follows the principle of transfer learning – customizing the pre-trained model to fit another task of same input type but different distortion levels – it is drastically different from existing practice of transfer learning: (i) GearNN actively identifies the “distortion-sensitive” weights and only fine-tunes such weights. In contrast, typical transfer learning fine-tunes either only the fully-connected layer or all DNN weights. (ii) GearNN enables partial weight adaptation in runtime inference, whereas typical transfer learning still uses a pre-trained DNN for inference.
Adaptive bitrate streaming. Adaptive bitrate (ABR) streaming quickly gains popularity in recent years [9, 13, 15]. By adapting the media stream quality in real-time following the dynamic network bandwidth, ABR streaming achieves both the high throughput and low latency. Unfortunately, existing DNN-based inference solutions mostly use a single DNN and fail to accommodate the dynamic quality levels on the ABR streams. In contrast, GearNN can adapt the DNN to accommodate dynamic streaming quality.
3 GearNN: Partial weight adaptation
In this section, we first analyze the effect of fine-tuning a DNN with distorted training data (§3.1), and then reveal our observation that the majority of DNN weights have minor changes after fine-tuning with distorted data (§3.2). Finally, we elaborate on the GearNN design, featuring partial DNN adaptation motivated by our observation (§3.3).
3.1 What happens when fine-tuning a DNN?
3.1.1 Frequency-domain features of visual distortions
Since human eyes are insensitive to the high-frequency components (in the spatial-frequency domain) of the visual input, encoders like JPEG and H.264 compress such high-freq. components aggressively. Similarly, after reducing the image brightness, human eyes still perceive object outlines (low-freq. components), but not textures (high-freq. components). In other words, typical visual distortions in practice essentially add noise to the high-freq. components.
3.1.2 DNN’s frequency response after fine-tuning
, we prepend an Inverse Discrete Fourier Transform (IDCT) module to the typical DNN training framework, then the frequency components (DCT coefficients) of the input become the leaf nodes, and we can perform backward propagation to obtain the gradient of lossw.r.t. every input frequency component. A higher gradient amplitude of a certain frequency component indicates that the DNN is more sensitive to input noise on this frequency, and we define the gradient map of all input frequency components as the DNN’s frequency response .
We then compare the frequency responses of the original DNN and the DNN fine-tuned with distorted training data. We repeat the comparison for 4 types of distortions: (i) H.264 compression with a quality (CRF) of 24; (ii) JPEG compression with a quality of 10; (iii) Underexposure with 10% brightness of the original image; (iv) Data augmentation that mixes H.264 frames with different qualities (CRF from 15 to 24) in one training set. Fig. 4 shows the measured DNN frequency responses. We see that, compared to the original DNN, the DNNs fine-tuned with distorted training data become less sensitive to the high-frequency components in all tested cases, i.e., they learn to avoid looking at the noisy high-frequency components for better robustness.
3.2 Distortion-sensitive DNN weights
Fine-tuning a DNN is updating its weights to fit a new training set. In §3.1, we reveal that fine-tuning a DNN with a distorted dataset is equivalent to reshaping its frequency response so that it filters out the noise from the distortion. In this section, we dig further to understand which DNN weights play more critical roles than other ones in reshaping the frequency response. We define such important weights as “distortion-sensitive”. It is worth noting that the “distortion-sensitivity” is different from the importance of neurons in DNN pruning. Some weights can be vital for the inference and should not be pruned. However, they can meanwhile be insensitive to the input distortion, i.e., their values do not change much after fine-tuning with distorted training data – there is no need to fine-tune them because the inference output is insensitive to the small changes of the DNN weights as shown in existing works [5, 3, 10].
3.2.1 Modeling DNN weights’ sensitivity to distortions
We first fine-tune a pre-trained DNN by using distorted training datasets, and compare the resulting weights with the original model. Let denote the number of layers in , and denotes each layer, which contains weights, then we have . Let denote the -th weight of the layer in the original pre-trained DNN, and denotes the fine-tuning process with a certain distortion level . Then, is the corresponding weight of the fine-tuned DNN, and we can compute the average change of weight values in layer , caused by fine-tuning, as:
The layers with a high value yield significant change of weight values, when fine-tuned to fit the distortion level , which means they are sensitive to the distortion level. Therefore, we define as the distortion sensitivity of layer . Following Eq. (1), we measure the layer-level weight change caused by the fine-tuning for three cases: DRN-D-38 fine-tuned with H.264 of quality level (CRF) 23 in Fig 4(a); Mask R-CNN fine-tuned with JPEG of quality level (Q) 10 in Fig 4(b); and, Mask R-CNN fine-tuned with dimmed images of relative brightness 0.2 in Fig 4(c).
In Fig. 4(a), the sum size of the DRN “weights” (including both the weights & biases)111We use the term “weights” to denote “DNN parameters including both weights & biases” for simplicity, except for Fig. 5. that changed more than after fine-tuning only accounts for 1.44% of the model size. Similarly, only and of the Mask R-CNN weights changed more than in Fig. 4(b) and Fig. 4(c). We thus have an important observation: only a tiny portion of DNN weights have non-negligible changes (e.g., ) after fine-tuning with distorted data. In order to fit DNN to the distorted inputs, we can simply reshape the frequency response of a DNN by changing such tiny portion of DNN weights.
3.2.2 Choosing subset of DNN weights to fine-tune
Since only a tiny portion of DNN weights have non-negligible changes after fine-tuning with distorted data, we propose to select and then fine-tune only these “distortion-sensitive” weights under a user-specified constraint on memory space. Then, this can be formulated as a knapsack problem, where the “item weight” of layer is its parameter number , while the “item value” of is its distortion sensitivity defined in Eq. (1). Now, the problem becomes selecting a list of DNN layers to maximize the total “value” (distortion sensitivity) , under the constraint that the total “size” (memory consumption) is within a user-defined bound , which is summarized in Eq. (3.2.2).
After obtaining the optimal list of layers, we fine-tune the corresponding DNN portion (we call it adaptor) with the dataset of quality while keeping the rest of the DNN (we call it the base ) frozen. This partial fine-tuning step can be denoted as .
3.2.3 Frequency response of partially fine-tuned DNN
We further compare the frequency responses of both partially and fully fine-tuned DNNs to understand the effect of partial fine-tuning. Our experiments include two models: DRN-D-38 with of weights fine-tuned (in Fig. 6) and Mask R-CNN with of weights fine-tuned (in Fig. 7). By comparing Fig. 6b and 6c, we observe that fine-tuning the entire DRN and fine-tuning only of the DRN weights with the same distorted dataset yield DNNs with close frequency responses – Both avoid looking at the noisy high-frequency components, compared to the original one in Fig. 6a. We have a similar observation from Fig. 7.
3.3 GearNN workflow
Based on the algorithm in §3.2, we design GearNN, a robust inference architecture that adapts only a small portion of the DNN, following the instantaneous input distortion.
Splitting the DNN (offline). The first step is to split the DNN into two parts – the adaptor to be changed in real-time following the input distortion level and the base that remains constant. The splitting follows §3.2.2: For each distortion level , GearNN fine-tunes the entire DNN using distorted training data and compares the result with the original DNN, to obtain the distortion-sensitivity metric of every DNN layer. Then, by solving the optimization problem in Eq. (3.2.2), GearNN picks a subset of layers to fine-tune, which is the adaptor for distortion level .
Partial fine-tuning (offline). GearNN then fine-tunes the adaptors, each using a dataset distorted to a particular level (the orignal dataset before distortion is the same as the one used in the DNN splitting step), while freezing the base, as illustrated in Fig. 8. In this way, GearNN obtains multiple fine-tuned adaptors , each fitting a particular input distortion level. Alg. 1 summarizes the above steps.
Partial adaptation (online) With the fine-tuned adaptors and the base, we can run GearNN online. Since the size of adaptors is tiny, an inference server can load multiple adaptors for all supported distorted levels at low memory cost. Next, given a visual input stream with various distortion levels (e.g., ABR streaming or receiving from heterogeneous IoT hardware), GearNN switches between the adaptors to fit the instantaneous input distortion level, while keeping the base weights unchanged, as shown in Fig. 1. It is straightforward to get the distortion level of the instantaneous input frame. For JPEG images, the quality level is directly embedded in the image. For DASH video streaming, the frame resolution is obviously given. For the brightness levels, we can directly compute the brightness of an image based on its pixel values. Overall, it is simple and fast to determine which adaptor to use for the current input frame, enabling the real-time adaptation.
Why adapting at the layer-level?
Adapting a subset of DNN weights requires not only their new values but also their positions in the model. For example, we can use a 0-1 vector to mark every weight (1 means do adaption and 0 means otherwise), then a DNN with millions of weights needs an extremely long vector, causing huge overhead. In contrast, labeling the DNN layers incurs tiny overhead since DNNs typically have up to hundreds of layers.
4.1 DNN-related configurations
We implement GearNN using PyTorch  and public datasets. While GearNN is designed as a generic inference architecture, we use two popular DNN models for its implementation: the dilated residual networks (DRN) [22, 21] and the Mask R-CNN model . Also, we run DRN on the Cityscapes dataset  for urban driving scenery semantic segmentation and Mask R-CNN on the Penn-Fudan dataset  for pedestrian segmentation and detection. GearNN uses a small learning rate of and runs only epochs when fine-tuning the adaptor as it only slightly updates the DNN trained with undistorted dataset.
Since GearNN allows a user to specify memory constraint, we evaluated GearNN with various adaptor sizes, ranging from 0.1% to 1% of the original DNN size, and our empirical results suggest that the adaptor with 0.2% of the original DRN size or 0.4% of the Mask R-CNN size best balance the inference accuracy and memory consumption.
4.2 Benchmark solutions
For benchmarks, we use 4 alternatives (also in Fig. 9):
Original DNN. The original DNN is a DNN pre-trained with the original undistorted dataset and is available in public. In our implementation, we download and use original DNN models from public links provided by their authors.
DNN switching. A straightforward solution to accommodate DNN inputs with various distortions is to train and use multiple DNNs, each for a particular distortion level.
Mixed training. A common technique to make DNN robust is to use perturbed images, via data augmentation, for training. We transform the original datasets to multiple versions, each for a particular distortion level, and then randomly sample from these distorted datasets to form a new dataset with mixed distortions. Finally, we fine-tune the DNN with the mixed dataset.
Stability training. Google proposed stability training  to improve the robustness of a DNN against noisy inputs. This solution forces a DNN to map inputs with small perturbations to their original input images to achieve the same inference output. Stability training only improves the training phase, and the inference still uses a constant DNN.
4.3 Distortion types
We now define input distortion types used in GearNN.
Resolution scaling. One primary motivation of GearNN is to make video analytics fit adaptive bitrate (ABR) video streaming, and the standard way of ABR streaming is to adapt the video resolution like MPEG-DASH . Downscaling the video dimension reduces its size and causes distortions. We use H.264 encoder to compress the Cityscapes dataset of original resolution to 6 different versions with smaller resolutions: , , , , , . All these frames share a constant rate factor (CRF) of 18, meaning small quantization loss.
JPEG compression. The quality of a JPEG image is defined by an integer value , ranging from to , and a higher indicates higher image quality and less distortion. We compress a lossless PNG-formatted dataset (like Cityscapes) to 10 different versions with JPEG quality levels, ranging from to in a step of , while keeping the same resolution as the undistorted dataset.
Brightness. A too bright or too dark visual input can significantly affect the DNN inference performance. We adjust the image brightness by Pillow to make multiple versions of the same dataset, each having a particular relative brightness to the original dataset. Our implementation includes relative brightness levels from 0.1 (10% of the original brightness) to 2.0 (double the original brightness), with the resolution unchanged.
GearNN tackles distorted inputs by fine-tuning adaptors for each resolution, compression level, brightness, or even combinations of them. In §5, we evaluate different distortion types separately to show the impact of each type.
We now present experimental results under different distortion types, datasets, tasks, and benchmarks.
5.1 JPEG quality level
In practical scenarios where a server (at edge or cloud) processes images from remote IoT devices, the inference engine at the server may encounter various quality levels of JPEG images as JPEG is the dominant image compression standard. In this context, we evaluate GearNN with inputs of various JPEG quality levels. We use the Cityscapes dataset, and build GearNN over DRN-D-22, a smaller DRN model than the one in §5.2, to show it is not limited to a particular model. Following §4.3, we convert the dataset to 10 versions with different JPEG quality levels and set the adaptor size as of the model size (§4.1), then the memory overhead is over the original DNN.
The results in Fig. 9(a) confirm the high inference accuracy of GearNN on all JPEG quality levels. In contrast, original DNN suffers from significant accuracy degradation at low quality levels, and its mIoU falls below 40% for quality under 50. Mixed training also achieves lower accuracy than GearNN does, especially on inputs with high & low qualities, because it attempts to map different distorted versions of an image to the same inference output, which is difficult for a single DNN (Fig. 2). DNN switching yields accuracy similar to GearNN’s, but at the cost of much higher memory cost (Table 1), as it needs to keep multiple DNNs in the memory. When each quality level has the same probability to appear in the input, GearNN achieves average accuracy gain over the original DNN and gain over the mixed training. The accuracy difference with the memory-hungry DNN switching is only .
|10 of JPEG qualities (DRN-D-22)||7 of H.264 resolutions (DRN-D-38)|
|Params||Overhead vs. original||Params||Overhead vs. original|
5.2 H.264 video resolution
We then evaluate how GearNN guarantees high inference accuracy across dynamic input video resolutions, by using the Cityscapes dataset, DRN-D-38 model, and the frame scaling with 7 resolution levels as distortions (§4.3) We set the adaptor size to of the model size (§4.1), then the memory overhead is over the original DNN. From the results in Fig. 9(b), we see that both GearNN and DNN switching significantly outperform the original DNN and mixed training, with around higher accuracy (mIoU) under resolutions lower than 1024222The DNN trained with original images achieves the highest accuracy at a smaller input size (Fig. 9(b)), which implies that scaling the object to match field-of-view of the DNN kernel may compensate for the information loss. Nevertheless, this observation is out of the scope of this paper.. Although GearNN and DNN switching enjoy similar accuracy, GearNN only costs a memory overhead over the original DNN (Table 1), while DNN switching requires keeping multiple DNNs in the memory and thus suffers from a high memory overhead. When each input resolution has an equal chance to appear, GearNN achieves higher average accuracy over the original DNN, over the mixed training, and over DNN switching.
5.3 Brightness level
Another application scenario of GearNN
is autonomous driving, where the camera can capture video frames with diverse brightness levels. In such a scenario, abrupt brightness change may cause even human eyes to malfunction temporarily. In this experiment, we reveal that the DNN-based computer vision also suffers from the same problem, andGearNN can address this by partially adapting the DNN to the brightness changes. Following §4.3, we alter the brightness of the datasets and use the DNR-D-38 model.
The results in Fig. 9(c) show the same performance ranking as the experiments above – GearNN and DNN switching achieve comparable accuracy, while mixed training suffers significant accuracy loss on high or low input brightness. Next, the original DNN has the worst performance on all brightness levels. Overall, considering the 20 brightness levels from 0.1 to 2.0, GearNN costs merely memory overhead, compared to the original DNN, while switching DNNs costs extra memory, rendering it impractical. If all brightness levels have the same chance of appearing in the input, GearNN achieves higher average accuracy than the original DNN, than the mixed training, and a slight accuracy difference over the memory-hungry DNN switching.
5.4 Different datasets and tasks
To demonstrate that GearNN is not bounded to a particular model or dataset, we run GearNN on pedestrian detection and segmentation with the Penn-Fudan dataset. We use Mask R-CNN with ResNet-50 as the backbone and perform both the segmentation and bounding box detection of the pedestrians. Our GearNN prototype runs on top of the Mask R-CNN model and supports 10 JPEG input quality levels (§4.3), for both the pedestrian segmentation (Fig. 13) and bounding box detection (Fig. 13). Since we set the adaptor size to of the model size (§4.1), the memory overhead is over the original DNN.
From the segmentation results in Fig. 13, we first see that the original DNN and mixed training suffer from severe accuracy loss when the input quality is below 20. GearNN has similar accuracy to DNN switching, as in all other experiments. As the input quality increases, the accuracy of the original DNN and mixed training remain below GearNN’s. But the gap is smaller than that of the Cityscapes dataset, because the PennFudan dataset has much lower resolution (around 300400 width & height), containing less information than Cityscapes (20481024), and thus, the input distortions cause less impact. In Fig. 13, we further plot the bounding box detection accuracy measured from the same set of experiments. The results show that GearNN always achieves higher accuracy than the original DNN and mixed training, especially at low input qualities. Meanwhile, it achieves similar accuracy, while maintaining much lower memory overhead than DNN switching.
Overall, if all JPEG quality levels have the same probability to appear in the input, GearNN achieves (segmentation) / (detection) higher average accuracy than the original DNN, (segmentation) / (detection) higher than the mixed training, and a small (segmentation) / (detection) accuracy difference from the memory-hungry DNN switching.
5.5 Comparison with stability training
There are only a few existing solutions in improving the DNN robustness to distorted/perturbed inputs. One of the most well-known works is the stability training proposed by Google. Therefore, we perform an extensive performance comparison against stability training under various configurations. In particular, we implement both GearNN and stability training based on the DRN-D-38 model and perform the evaluation on the Cityscapes dataset compressed by the H.264 encoder. For stability training, there are two configurable parameters: (i) controls the tradeoff between the stability and the accuracy on undistorted inputs, and (ii) controls the tolerance to distortions. We test multiple combinations of and , then show the results in Fig. 13.
We first observe that GearNN outperforms all tested combinations of and at any input distortion level. We also see that and control the performance of stability training as expected. Overall, a higher or leads to better tolerance to distortions. It essentially sacrifices the performance on the low-distortion inputs to improve the performance on the high-distortion inputs, so that the overall performance is balanced. Compared to the stability training configuration with the highest accuracy when and , GearNN achieves average accuracy gain, while the highest accuracy gain is at the CRF of 24 and at the CRF of 15. In sum, stability training uses a single DNN to fit a wide range of distortions, and thus it has to tradeoff performance across different distortion levels. In contrast, GearNN partially adapts the weights to fit the current input distortion level, which guarantees its high accuracy and low memory overhead.
In this paper, we present GearNN, a memory-efficient adaptive DNN inference architecture, to combat the diverse input distortions. GearNN enables robust inference under a broad range of input distortions, without compromising memory consumption. It identifies and adapts only the distortion-sensitive DNN parameters, a tiny portion of the DNN (e.g., of the total size), following the instantaneous input distortion level. Our evaluation demonstrates the superior performance of GearNN over benchmark solutions such as stability training from Google, when the distortion level of input varies due to adaptive video resolution, JPEG compression, or brightness change. As a general inference architecture, GearNN can be potentially applied to many existing DNN models and enables them to accommodate the diverse input distortions in the IoT era.
Adaptive neural networks for efficient inference.
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