Deep Representation Debiasing via Mutual Information Minimization and Maximization (Student Abstract)
Keywords:Representation Learning, Debiasing, Mutual Information
AbstractDeep representation learning has succeeded in several fields. However, pre-trained deep representations are usually biased and make downstream models sensitive to different attributes. In this work, we propose a post-processing unsupervised deep representation debiasing algorithm, DeepMinMax, which can obtain unbiased representations directly from pre-trained representations without re-training or fine-tuning the entire model. The experimental results on synthetic and real-world datasets indicate that DeepMinMax outperforms the existing state-of-the-art algorithms on downstream tasks.
How to Cite
Han, R., Wang, W., Long, Y., & Peng, J. (2022). Deep Representation Debiasing via Mutual Information Minimization and Maximization (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12965-12966. https://doi.org/10.1609/aaai.v36i11.21619
AAAI Student Abstract and Poster Program