  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.
