AAAI Conference 2022 Short Paper
Deep Representation Debiasing via Mutual Information Minimization and Maximization (Student Abstract)
- Ruijiang Han
- Wei Wang
- Yuxi Long
- Jiajie Peng
Deep 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, DeepMin- Max, 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.