Publisher
Springer Nature Switzerland
Reference9 articles.
1. Nagananda, N., et al.: Benchmarking domain adaptation methods on aerial datasets. Sensors (2021)
2. Taufique, A., Jahan, C.S., Savakis, A.: Unsupervised continual learning for gradually varying domains. In: Computer Vision and Pattern Recognition (CVPR) Workshop on Continual Learning in Computer Vision (CLVision) (2022)
3. Taufique, A., Jahan, C.S., Savakis, A.: Continual unsupervised domain adaptation in data-constrained environments. IEEE Trans. Artifi. Intell. (2023)
4. Liang, J., Hu, D., Feng, J.: Do we really need to access the source data? Source hypothesis transfer for unsupervised domain adaptation. In: International Conference on Machine Learning (ICML), pp. 6028–6039. PMLR (2020)
5. Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) Computer Vision – ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part IV, pp. 213–226. Springer Berlin Heidelberg, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_16