Open Compound Domain Adaptation
| Autor: | Boqing Gong, Xingang Pan, Xiaohang Zhan, Ziwei Liu, Dahua Lin, Zhongqi Miao, Stella X. Yu |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Generalization Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Machine Learning (stat.ML) 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Data modeling Domain (software engineering) Machine Learning (cs.LG) Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer 0105 earth and related environmental sciences Test data |
| Zdroj: | CVPR |
| DOI: | 10.48550/arxiv.1909.03403 |
| Popis: | A typical domain adaptation approach is to adapt models trained on the annotated data in a source domain (e.g., sunny weather) for achieving high performance on the test data in a target domain (e.g., rainy weather). Whether the target contains a single homogeneous domain or multiple heterogeneous domains, existing works always assume that there exist clear distinctions between the domains, which is often not true in practice (e.g., changes in weather). We study an open compound domain adaptation (OCDA) problem, in which the target is a compound of multiple homogeneous domains without domain labels, reflecting realistic data collection from mixed and novel situations. We propose a new approach based on two technical insights into OCDA: 1) a curriculum domain adaptation strategy to bootstrap generalization across domains in a data-driven self-organizing fashion and 2) a memory module to increase the model's agility towards novel domains. Our experiments on digit classification, facial expression recognition, semantic segmentation, and reinforcement learning demonstrate the effectiveness of our approach. Comment: To appear in CVPR 2020 as an oral presentation. Code, datasets and models are available at: https://liuziwei7.github.io/projects/CompoundDomain.html |
| Databáze: | OpenAIRE |
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