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Learning Causal Representations for Robust Domain Adaptation
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AI
DOI：10.1109/TKDE.2021.3119185.png)
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摘要
En 中文
In this study, we investigate a challenging problem, namely, robust domain adaptation, where data from only a single well-labeled source domain are available in the training phase. To address this problem, assuming that the causal relationships between the features and the class variable are robust across domains, we propose a novel causal autoencoder (CAE), which integrates a deep autoencoder and a causal structure learning model to learn causal representations using data from a single source domain. Specifically, a deep autoencoder model is adopted to learn the low-dimensional representations, and a causal structure learning model is designed to separate the low-dimensional representations into two groups: causal representations and task-irrelevant representations. Using three real-world datasets, the experiments have validated the effectiveness of CAE, in comparison with eleven state-of-the-art methods.
Keyword:
Dogs
Data models
Predictive models
Markov processes
Adaptation models
Training
Sentiment analysis
Domain adaptation
causal discovery
autoencoder
期刊
IF:
10.4
论文数:
6.9K
被引数:
3.2W
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