Domain Adaptation with Adversarial Training on Penultimate Activations


  • Tao Sun Stony Brook University
  • Cheng Lu XPeng Motors
  • Haibin Ling Stony Brook University



ML: Transfer, Domain Adaptation, Multi-Task Learning, ML: Adversarial Learning & Robustness, ML: Classification and Regression


Enhancing model prediction confidence on target data is an important objective in Unsupervised Domain Adaptation (UDA). In this paper, we explore adversarial training on penultimate activations, i.e., input features of the final linear classification layer. We show that this strategy is more efficient and better correlated with the objective of boosting prediction confidence than adversarial training on input images or intermediate features, as used in previous works. Furthermore, with activation normalization commonly used in domain adaptation to reduce domain gap, we derive two variants and systematically analyze the effects of normalization on our adversarial training. This is illustrated both in theory and through empirical analysis on real adaptation tasks. Extensive experiments are conducted on popular UDA benchmarks under both standard setting and source-data free setting. The results validate that our method achieves the best scores against previous arts. Code is available at




How to Cite

Sun, T., Lu, C., & Ling, H. (2023). Domain Adaptation with Adversarial Training on Penultimate Activations. Proceedings of the AAAI Conference on Artificial Intelligence, 37(8), 9935-9943.



AAAI Technical Track on Machine Learning III