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View Code? Open in Web Editor NEW[CVPR21] Dynamic Class Queue for Large Scale Face Recognition in the Wild
[CVPR21] Dynamic Class Queue for Large Scale Face Recognition in the Wild
Hi, nice work about face recognition! I'm only confused about the following:
In Section 3.1. Preliminaries Classification-based Representation Learning., as the paper says, "During evaluation, the FC layer is removed and only the feature extractor is used."
But in the code
Line 146 in 5422b63
It uses the weight generator network(encoder_k) to get the feature(or weight?) for evaluation; I am very puzzled here. Is there something that I missed?
在DCQ的前向中,对im_k做了一次shuff(对应函数_batch_shuffle_ddp),然后又做了一次unshuff(对应函数_batch_unshuffle_ddp),我认为得到的结果和直接对im_k做前向的结果是一样的。这样做的目的是什么?
我觉得这个方法中的weight类似于特征,类似于说今天维护了一个特征的队列,然后加上了triplet loss去做,也就是没有weight。我认为本质上是用triplet loss来训练人脸识别,然后加上momentum。
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