Modern computer vision requires processing large amounts of data, both while training the model and/or during inference, once the model is deployed. Scenarios where images are captured and processed in physically separated locations are increasingly common (e.g. autonomous vehicles, cloud computing, smartphones). In addition, many devices suffer from limited resources to store or transmit data (e.g. storage space, channel capacity). In these scenarios, lossy image compression plays a crucial role to effectively increase the number of images collected under such constraints. However, lossy compression entails some undesired degradation of the data that may harm the performance of the downstream analysis task at hand, since important semantic information may be lost in the process. Moreover, we may only have compressed images at training time but are able to use original images at inference time ( i.e. test), or vice versa, and in such a case, the downstream model suffers from covariate shift. In this paper, we analyze this phenomenon, with a special focus on vision-based perception for autonomous driving as a paradigmatic scenario. We see that loss of semantic information and covariate shift do indeed exist, resulting in a drop in performance that depends on the compression rate. In order to address the problem, we propose dataset restoration, based on image restoration with generative adversarial networks (GANs). Our method is agnostic to both the particular image compression method and the downstream task; and has the advantage of not adding additional cost to the deployed models, which is particularly important in resource-limited devices. The presented experiments focus on semantic segmentation as a challenging use case, cover a broad range of compression rates and diverse datasets, and show how our method is able to significantly alleviate the negative effects of compression on the downstream visual task.

中文翻译：

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压缩图像的分布式学习和推理

一旦训练了模型，现代计算机视觉就需要在训练模型时和/或在推理过程中处理大量数据。在物理上分开的位置捕获和处理图像的场景越来越普遍（例如，自动驾驶汽车，云计算，智能手机）。另外，许多设备遭受有限的资源来存储或传输数据（例如，存储空间，信道容量）。在这些情况下，有损图像压缩在有效增加此类约束条件下收集的图像数量方面起着至关重要的作用。但是，有损压缩会导致数据的某些不良降级，这可能会损害手头的下游分析任务的性能，因为在此过程中可能会丢失重要的语义信息。而且， IE测试），反之亦然，在这种情况下，下游模型会发生协变量偏移。在本文中，我们分析了这种现象，并特别关注基于视觉的自动驾驶作为范例场景的感知。我们看到确实存在语义信息的丢失和协变量偏移，从而导致取决于压缩率的性能下降。为了解决该问题，我们提出了基于生成对抗网络（GAN）的图像恢复的数据集恢复。我们的方法与特定的图像压缩方法和下游任务无关。并具有不为部署的模型增加额外成本的优点，这在资源受限的设备中尤其重要。提出的实验着重于语义分割作为具有挑战性的用例，