Task-customized Masked AutoEncoder via Mixture of Cluster-conditional
  Experts
Zhili Liu Kai Chen Jianhua Han Lanqing Hong Hang Xu Zhenguo Li James T. Kwok
Masked Autoencoder~(MAE) is a prevailing self-supervised learning method that
achieves promising results in model pre-training. However, when the various
downstream tasks have data distributions different from the pre-training data,
the semantically irrelevant pre-training information might result in negative
transfer, impeding MAE's scalability. To address this issue, we propose a novel
MAE-based pre-training paradigm, Mixture of Cluster-conditional Experts (MoCE),
which can be trained once but provides customized pre-training models for
diverse downstream tasks. Different from the mixture of experts (MoE), our MoCE
trains each expert only with semantically relevant images by using
cluster-conditional gates. Thus, each downstream task can be allocated to its
customized model pre-trained with data most similar to the downstream data.
Experiments on a collection of 11 downstream tasks show that MoCE outperforms
the vanilla MAE by 2.45\% on average. It also obtains new state-of-the-art
self-supervised learning results on detection and segmentation.