Open Domain Generalization with a Single Network by Regularization Exploiting Pre-trained Features
Keywords: data distribution shifts, domain generalization, open-set recognition
TL;DR: A regularization method for domain generalization to tackle both distribution shifts and category shifts problem
Abstract: Open Domain Generalization (ODG) is a challenging task as it not only deals with distribution shifts but also category shifts between the source and target datasets. To handle this task, the model has to learn a generalizable representation that can be applied to unseen domains while also identify unknown classes that were not present during training. Previous work has used multiple source-specific networks, which involve a high computation cost. Therefore, this paper proposes a method that can handle ODG using only a single network. The proposed method utilizes a head that is pre-trained by linear-probing and employs two regularization terms, each targeting the regularization of feature extractor and the classification head, respectively. The two regularization terms fully utilize the pre-trained features and collaborate to modify the head of the model without excessively altering the feature extractor. This ensures a smoother softmax output and prevents the model from being biased towards the source domains. The proposed method shows improved adaptability to unseen domains and increased capability to detect unseen classes as well. Extensive experiments show that our method achieves competitive performance in several benchmarks.
Primary Subject Area: Impact of data bias, variance, and drifts
Paper Type: Research paper: up to 8 pages
Participation Mode: Virtual
Confirmation: I have read and agree with the workshop's policy on behalf of myself and my co-authors.
Submission Number: 2
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