Summary of Model Debiasing by Learnable Data Augmentation, By Pietro Morerio et al.
Model Debiasing by Learnable Data Augmentation
by Pietro Morerio, Ruggero Ragonesi, Vittorio Murino
First submitted to arxiv on: 9 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the challenge of learning from biased data in an unsupervised scenario, where the bias is unknown. Existing deep neural networks can efficiently fit training data but struggle with poor generalization capabilities when faced with biased data. The proposed 2-stage learning pipeline features a data augmentation strategy that regularizes the training process. First, biased and unbiased samples are identified by training over-biased models. Second, the subdivision is exploited within a data augmentation framework, combining original samples while learning mixing parameters that have a regularization effect. Experimental results on synthetic and realistic datasets show state-of-the-art classification accuracy, outperforming competing methods and achieving robust performance on both biased and unbiased examples. The proposed method is agnostic to the level of bias in the data, improving model generalization regardless of the presence or absence of bias. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to learn a new skill from a teacher who has some biases. You want to learn the real skill, not just what the teacher thinks is important. This paper solves this problem by finding ways to ignore the biases and focus on the real task at hand. It proposes a new way of learning that first identifies biased and unbiased parts of the data, then uses those findings to mix the original samples in a special way. This helps the model learn more accurately and makes it work better even when there are no biases present. The results show that this method is much better than previous ones at recognizing patterns in both biased and unbiased data. |
Keywords
» Artificial intelligence » Classification » Data augmentation » Generalization » Regularization » Unsupervised
