Abstract
Domain Adaptation (DA) techniques are important for overcoming the domain shift between the training dataset (called source domain) and the testing dataset (called target domain). Standard DA methods assume that the entire target domain is available during adaptation, but this assumption is often violated in practice. We consider DA in a data constrained scenario, where target data become available in small batches over time, and adaptation takes place continually. Hence, continual DA is a framework to instantiate the Dynamic Data Driven Applications Systems (DDDAS) paradigm, wherein a model is developed from the data available to discern the relevant features, and subsequently when the model is deployed, it needs to be adapted (i.e., through a learning process) from the new real-world data. We discuss a novel source-free method for Continual Domain Adaptation (ConDA) that utilizes a buffer for selective replay of previously seen samples. In our unsupervised adaptation framework, we selectively mix samples from incoming batches with data stored in a buffer and use them to adapt our model as new batches are received. Our results using ConDA demonstrate the benefits of our framework when operating in data constrained environments.
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References
Nagananda, N., et al.: Benchmarking domain adaptation methods on aerial datasets. Sensors (2021)
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)
Taufique, A., Jahan, C.S., Savakis, A.: Continual unsupervised domain adaptation in data-constrained environments. IEEE Trans. Artifi. Intell. (2023)
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)
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
Venkateswara, H., Eusebio, J., Chakraborty, S., Panchanathan, S.: Deep hashing network for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5018– 5027 (2017)
Peng, X., Usman, B., Kaushik, N., Hoffman, J., Wang, D., Saenko, K.: VisDA: the visual domain adaptation challenge. arXiv preprint arXiv:1710.06924, (2017)
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Piotr Dollár, C., Zitnick, L.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision – ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V, pp. 740–755. Springer International Publishing, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Taufique, A., Jahan, C.S., Savakis, A.: ConDA: Continual Unsupervised Domain Adaptation, arXiv preprint arXiv:2103.11056, (2021)
Acknowledgments
The author acknowledges the valuable contributions by his students Abu Taufique and Chowdhury Sadman Jahan, and would like to thank Dr. Erik Blasch for participating in the research conceptualization. The research was partly supported by the Air Force Office of Scientific Research (AFOSR) grant FA9550–20-1–0039.
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Savakis, A. (2024). Towards Continual Unsupervised Data Driven Adaptive Learning. In: Blasch, E., Darema, F., Aved, A. (eds) Dynamic Data Driven Applications Systems. DDDAS 2022. Lecture Notes in Computer Science, vol 13984. Springer, Cham. https://doi.org/10.1007/978-3-031-52670-1_35
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DOI: https://doi.org/10.1007/978-3-031-52670-1_35
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