Papers › Entropy Minimization vs. Diversity Maximization for Domain Adaptation
Entropy Minimization vs. Diversity Maximization for Domain Adaptation
Xiaofu Wu, Suofei hang, Quan Zhou, Zhen Yang, Chunming Zhao, Longin Jan Latecki
Entropy minimization has been widely used in unsupervised domain adaptation (UDA). However, existing works reveal that entropy minimization only may result into collapsed trivial solutions. In this paper, we propose to avoid trivial solutions by further introducing diversity maximization. In order to achieve the possible minimum target risk for UDA, we show that diversity maximization should be elaborately balanced with entropy minimization, the degree of which can be finely controlled with the use of deep embedded validation in an unsupervised manner. The proposed minimal-entropy diversity maximization (MEDM) can be directly implemented by stochastic gradient descent without use of adversarial learning. Empirical evidence demonstrates that MEDM outperforms the state-of-the-art methods on four popular domain adaptation datasets.
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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 | MEDM | Accuracy | 88.9 | #8 of 17 | Archive leaderboard | report |
| Domain Adaptation | Office-31 | MEDM | Average Accuracy | 89.2 | #20 of 40 | Archive leaderboard | report |
| Domain Adaptation | Office-Home | MEDM | Accuracy | 69.5 | #25 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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