Papers › Learning Transferable Features with Deep Adaptation Networks
Learning Transferable Features with Deep Adaptation Networks
Mingsheng Long, Yue Cao, Jian-Min Wang, Michael. I. Jordan
Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation Network (DAN) architecture, which generalizes deep convolutional neural network to the domain adaptation scenario. In DAN, hidden representations of all task-specific layers are embedded in a reproducing kernel Hilbert space where the mean embeddings of different domain distributions can be explicitly matched. The domain discrepancy is further reduced using an optimal multi-kernel selection method for mean embedding matching. DAN can learn transferable features with statistical guarantees, and can scale linearly by unbiased estimate of kernel embedding. Extensive empirical evidence shows that the proposed architecture yields state-of-the-art image classification error rates on standard domain adaptation benchmarks.
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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 | DAN | Accuracy | 76.9 | #17 of 17 | Archive leaderboard | report |
| Domain Adaptation | MNIST-to-MNIST-M | MMD [tzeng2015ddc]; [long2015learning] | Accuracy | 76.9 | #5 of 5 | Archive leaderboard | report |
| Domain Adaptation | Office-Caltech | DAN[[Long et al.2015]] | Average Accuracy | 90.1 | #4 of 8 | Archive leaderboard | report |
| Domain Adaptation | SVNH-to-MNIST | MMD [tzeng2015ddc]; [long2015learning] | Accuracy | 71.1 | #8 of 9 | Archive leaderboard | report |
| Domain Adaptation | SYNSIG-to-GTSRB | DAN | Accuracy | 91.1 | #5 of 6 | Archive leaderboard | report |
| Domain Adaptation | Synth Digits-to-SVHN | MMD [tzeng2015ddc]; [long2015learning] | Accuracy | 88.0 | #3 of 4 | Archive leaderboard | report |
| Domain Adaptation | Synth Signs-to-GTSRB | MMD [tzeng2015ddc]; [long2015learning] | Accuracy | 91.1 | #3 of 4 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Office-Home | DAN [cite:ICML15DAN] | Accuracy | 74.3 | #15 of 20 | 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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