  Generalized category discovery (GCD) aims at addressing a more realistic and
challenging setting of semi-supervised learning, where only part of the
category labels are assigned to certain training samples. Previous methods
generally employ naive contrastive learning or unsupervised clustering scheme
for all the samples. Nevertheless, they usually ignore the inherent critical
information within the historical predictions of the model being trained.
Specifically, we empirically reveal that a significant number of salient
unlabeled samples yield consistent historical predictions corresponding to
their ground truth category. From this observation, we propose a Memory
Consistency guided Divide-and-conquer Learning framework (MCDL). In this
framework, we introduce two memory banks to record historical prediction of
unlabeled data, which are exploited to measure the credibility of each sample
in terms of its prediction consistency. With the guidance of credibility, we
can design a divide-and-conquer learning strategy to fully utilize the
discriminative information of unlabeled data while alleviating the negative
influence of noisy labels. Extensive experimental results on multiple
benchmarks demonstrate the generality and superiority of our method, where our
method outperforms state-of-the-art models by a large margin on both seen and
unseen classes of the generic image recognition and challenging semantic shift
settings (i.e.,with +8.4% gain on CUB and +8.1% on Standford Cars).
