Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs

• URL: http://arxiv.org/abs/2206.04674v1
• Date: Thu, 9 Jun 2022 17:59:59 GMT
• Title: Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs
• Authors: Jinguo Zhu, Xizhou Zhu, Wenhai Wang, Xiaohua Wang, Hongsheng Li, Xiaogang Wang, Jifeng Dai
• Abstract summary: We find that interference among different tasks and modalities is the main factor to this phenomenon. We introduce the Conditional Mixture-of-Experts (Conditional MoEs) to generalist models. Code and pre-trained generalist models shall be released.
• Score: 63.936622239286685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To build an artificial neural network like the biological intelligence system, recent works have unified numerous tasks into a generalist model, which can process various tasks with shared parameters and do not have any task-specific modules. While generalist models achieve promising results on various benchmarks, they have performance degradation on some tasks compared with task-specialized models. In this work, we find that interference among different tasks and modalities is the main factor to this phenomenon. To mitigate such interference, we introduce the Conditional Mixture-of-Experts (Conditional MoEs) to generalist models. Routing strategies under different levels of conditions are proposed to take both the training/inference cost and generalization ability into account. By incorporating the proposed Conditional MoEs, the recently proposed generalist model Uni-Perceiver can effectively mitigate the interference across tasks and modalities, and achieves state-of-the-art results on a series of downstream tasks via prompt tuning on 1% of downstream data. Moreover, the introduction of Conditional MoEs still holds the generalization ability of generalist models to conduct zero-shot inference on new tasks, e.g., video-text retrieval and video caption. Code and pre-trained generalist models shall be released.

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