Task-customized Masked AutoEncoder via Mixture of Cluster-conditional Experts

Autor:	Liu, Zhili, Chen, Kai, Han, Jianhua, Hong, Lanqing, Xu, Hang, Li, Zhenguo, Kwok, James T.
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted by ICLR 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.05382 Zobrazit plný text záznamu View this record from Arxiv