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

Wei-Hong Li; Xialei Liu; Hakan Bilen

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arXiv · 2204.02744

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

Wei-Hong Li·Xialei Liu·Hakan Bilen

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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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BibTeX RIS

Wei-Hong Li, Xialei Liu, Hakan Bilen. 2022-04-06. Universal Representations: A Unified Look at Multiple Task and Domain Learning. https://arxiv.org/abs/2204.02744

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