  Person re-identification (re-ID), is a challenging task due to the high
variance within identity samples and imaging conditions. Although recent
advances in deep learning have achieved remarkable accuracy in settled scenes,
i.e., source domain, few works can generalize well on the unseen target domain.
One popular solution is assigning unlabeled target images with pseudo labels by
clustering, and then retraining the model. However, clustering methods tend to
introduce noisy labels and discard low confidence samples as outliers, which
may hinder the retraining process and thus limit the generalization ability. In
this study, we argue that by explicitly adding a sample filtering procedure
after the clustering, the mined examples can be much more efficiently used. To
this end, we design an asymmetric co-teaching framework, which resists noisy
labels by cooperating two models to select data with possibly clean labels for
each other. Meanwhile, one of the models receives samples as pure as possible,
while the other takes in samples as diverse as possible. This procedure
encourages that the selected training samples can be both clean and
miscellaneous, and that the two models can promote each other iteratively.
Extensive experiments show that the proposed framework can consistently benefit
most clustering-based methods, and boost the state-of-the-art adaptation
accuracy. Our code is available at
https://github.com/FlyingRoastDuck/ACT_AAAI20.
