Papers › Attending to Discriminative Certainty for Domain Adaptation
Attending to Discriminative Certainty for Domain Adaptation
Vinod Kumar Kurmi, Shanu Kumar, Vinay P. Namboodiri
In this paper, we aim to solve for unsupervised domain adaptation of classifiers where we have access to label information for the source domain while these are not available for a target domain. While various methods have been proposed for solving these including adversarial discriminator based methods, most approaches have focused on the entire image based domain adaptation. In an image, there would be regions that can be adapted better, for instance, the foreground object may be similar in nature. To obtain such regions, we propose methods that consider the probabilistic certainty estimate of various regions and specify focus on these during classification for adaptation. We observe that just by incorporating the probabilistic certainty of the discriminator while training the classifier, we are able to obtain state of the art results on various datasets as compared against all the recent methods. We provide a thorough empirical analysis of the method by providing ablation analysis, statistical significance test, and visualization of the attention maps and t-SNE embeddings. These evaluations convincingly demonstrate the effectiveness of the proposed approach.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Domain Adaptation | ImageCLEF-DA | CADA-P | Accuracy | 88.3 | #12 of 17 | Archive leaderboard | report |
| Domain Adaptation | Office-31 | CADA-P | Average Accuracy | 89.5 | #19 of 40 | Archive leaderboard | report |
| Domain Adaptation | Office-Home | CADA | Accuracy | 70.2 | #23 of 29 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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