Universal Representations: A Unified Look at Multiple Task and Domain Learning

• URL: http://arxiv.org/abs/2204.02744v1
• Date: Wed, 6 Apr 2022 11:40:01 GMT
• Title: Universal Representations: A Unified Look at Multiple Task and Domain Learning
• Authors: Wei-Hong Li, Xialei Liu, Hakan Bilen
• Abstract summary: We propose a unified look at jointly learning multiple vision tasks and visual domains through universal representations. We show that universal representations achieve state-of-the-art performances in learning of multiple dense prediction problems. We also conduct multiple analysis through ablation and qualitative studies.
• Score: 37.27708297562079
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a unified look at jointly learning multiple vision tasks and visual domains through universal representations, a single deep neural network. Learning multiple problems simultaneously involves minimizing a weighted sum of multiple loss functions with different magnitudes and characteristics and thus results in unbalanced state of one loss dominating the optimization and poor results compared to learning a separate model for each problem. To this end, we propose distilling knowledge of multiple task/domain-specific networks into a single deep neural network after aligning its representations with the task/domain-specific ones through small capacity adapters. We rigorously show that universal representations achieve state-of-the-art performances in learning of multiple dense prediction problems in NYU-v2 and Cityscapes, multiple image classification problems from diverse domains in Visual Decathlon Dataset and cross-domain few-shot learning in MetaDataset. Finally we also conduct multiple analysis through ablation and qualitative studies.

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