Improving Image-recognition Edge Caches with a Generative Adversarial Network

TL;DR

This paper proposes using a GAN-based image translation technique to generate daytime images from nighttime photos, improving cache matching for image recognition and reducing cloud offloading latency.

Contribution

It introduces a novel application of ToDayGAN for enhancing edge caches in image recognition by generating synthetic daytime images from nighttime photos.

Findings

Reduces cloud offloading in image recognition tasks.

Decreases application latency by improving cache hit rates.

Demonstrates effectiveness of GAN-based image translation in edge caching.

Abstract

Image recognition is an essential task in several mobile applications. For instance, a smartphone can process a landmark photo to gather more information about its location. If the device does not have enough computational resources available, it offloads the processing task to a cloud infrastructure. Although this approach solves resource shortages, it introduces a communication delay. Image-recognition caches on the Internet's edge can mitigate this problem. These caches run on servers close to mobile devices and stores information about previously recognized images. If the server receives a request with a photo stored in its cache, it replies to the device, avoiding cloud offloading. The main challenge for this cache is to verify if the received image matches a stored one. Furthermore, for outdoor photos, it is difficult to compare them if one was taken in the daytime and the other at nighttime. In that case, the cache might wrongly infer that they refer to different places, offloading the processing to the cloud. This work shows that a well-known generative adversarial network, called ToDayGAN, can solve this problem by generating daytime images using nighttime ones. We can thus use this translation to populate a cache with synthetic photos that can help image matching. We show that our solution reduces cloud offloading and, therefore, the application's latency.