Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All in One Classifier
Unsupervised domain adaptation (UDA) has proven to be highly effective in transferring knowledge from a label-rich source domain to a label-scarce target domain. However, the presence of additional novel categories in the target domain has led to the development of open-set domain adaptation (ODA) and universal domain adaptation (UNDA). Existing ODA and UNDA methods treat all novel categories as a single, unified unknown class and attempt to detect it during training. However, we found that domain variance can lead to more significant view-noise in unsupervised data augmentation, which affects the effectiveness of contrastive learning (CL) and causes the model to be overconfident in novel category discovery. To address these issues, a framework nameded Soft-contrastive All-in-one Network (SAN) is proposed for ODA and UNDA tasks. SAN includes a novel data-augmentation-based soft contrastive learning (SCL) loss to fine-tune the backbone for feature transfer and a more human-intuitive classifier to improve new class discovery capability. The SCL loss weakens the adverse effects of the data augmentation view-noise problem which is amplified in domain transfer tasks. The All-in-One (AIO) classifier overcomes the overconfidence problem of current mainstream closed-set and open-set classifiers. Visualization and ablation experiments demonstrate the effectiveness of the proposed innovations. Furthermore, extensive experiment results on ODA and UNDA show that SAN outperforms existing state-of-the-art methods.
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Datasets
Office-Home
DomainNet
Office-31
VisDA-2017
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Universal Domain Adaptation | DomainNet | SAN | H-Score | 52.0 | # 4 | |
| Source-free | no | # 1 | ||||
| Universal Domain Adaptation | Office-31 | SAN | H-score | 91.8 | # 3 | |
| Source-Free | no | # 1 | ||||
| Universal Domain Adaptation | Office-Home | SAN | H-Score | 75.9 | # 4 | |
| Source-free | no | # 1 | ||||
| Universal Domain Adaptation | VisDA2017 | SAN | H-score | 60.1 | # 5 | |
| Source-free | no | # 1 |
