# Improving Image-recognition Edge Caches with a Generative Adversarial Network

arXiv: https://arxiv.org/abs/2202.05929 · HTML: https://mlsanity.com/paper/2202.05929/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 11 Feb 2022.

## Builds on (most cited first, 10 of 10)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Semantic Image Inpainting with Deep Generative Models](https://mlsanity.com/paper/1607.07539/) (2016)
- [CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms](https://mlsanity.com/paper/1706.07068/) (2017)
- [Benchmark Analysis of Representative Deep Neural Network Architectures](https://mlsanity.com/paper/1810.00736/) (2018)
- [Night-to-Day Image Translation for Retrieval-based Localization](https://mlsanity.com/paper/1809.09767/) (2018)
- [DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car](https://mlsanity.com/paper/1712.08644/) (2017)
- [Calibration-Aided Edge Inference Offloading via Adaptive Model Partitioning of Deep Neural Networks](https://mlsanity.com/paper/2010.16335/) (2020)

## Abstract (arXiv)

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.