PADCLIP: Pseudo-labeling with Adaptive Debiasing in CLIP for Unsupervised Domain Adaptation
Traditional Unsupervised Domain Adaptation (UDA) leverages the labeled source domain to tackle the learning tasks on the unlabeled target domain. It can be more challenging when a large domain gap exists between the source and the target domain. A more practical setting is to utilize a large-scale pre-trained model to fill the domain gap. For example, CLIP shows promising zero-shot generalizability to bridge the gap. However, after applying traditional fine-tuning to specifically adjust CLIP on a target domain, CLIP suffers from catastrophic forgetting issues where the new domain knowledge can quickly override CLIP's pre-trained knowledge and decreases the accuracy by half. We propose Catastrophic Forgetting Measurement (CFM) to adjust the learning rate to avoid excessive training (thus mitigating the catastrophic forgetting issue). We then utilize CLIP's zero-shot prediction to formulate a Pseudo-labeling setting with Adaptive Debiasing in CLIP (PADCLIP) by adjusting causal inference with our momentum and CFM. Our PADCLIP allows end-to-end training on source and target domains without extra overhead, and we achieved the best results on four public datasets, with a significant improvement (+18.5% accuracy) on DomainNet.
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Datasets
ImageNet
Office-Home
DomainNet
Office-31
VisDA-2017
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Unsupervised Domain Adaptation | DomainNet | PADCLIP | Accuracy | 63.7 | # 3 |
