Distributed Learning and Inference with Compressed Images
- URL: http://arxiv.org/abs/2004.10497v2
- Date: Fri, 5 Feb 2021 11:45:05 GMT
- Title: Distributed Learning and Inference with Compressed Images
- Authors: Sudeep Katakol, Basem Elbarashy, Luis Herranz, Joost van de Weijer,
and Antonio M. Lopez
- Abstract summary: This paper focuses on vision-based perception for autonomous driving as a paradigmatic scenario.
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.
- Score: 40.07509530656681
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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).
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, 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.
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