利用生成对抗网络改进图像识别边缘缓存
Improving Image-recognition Edge Caches with a Generative Adversarial Network
摘要：
图像识别是许多移动应用中的一项重要任务。例如，智能手机可以处理地标照片以收集有关其位置的更多信息。如果设备没有足够的计算资源可用，它会将处理任务卸载到云基础设施。虽然这种方法解决了资源短缺，但它引入了通信延迟。互联网边缘的图像识别缓存可以缓解这个问题。这些缓存运行在靠近移动设备的服务器上，并存储有关先前识别的图像的信息。如果服务器收到一个请求，其中有一张照片存储在其缓存中，它会回复设备，避免云卸载。此缓存的主要挑战是验证接收到的图像是否与存储的图像匹配。此外，对于户外照片，如果一张是在白天拍摄的，另一张是在夜间拍摄的，则很难比较它们。在这种情况下，缓存可能会错误地推断它们指向不同的位置，从而将处理工作卸载到云。这项工作表明，一个著名的生成对抗网络，称为ToDayGAN，可以通过使用夜间图像生成白天图像来解决这个问题。因此，我们可以使用这种转换来填充一个缓存与合成照片，可以帮助图像匹配。我们表明，我们的解决方案减少了云卸载，因此，应用程序的延迟。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
