Summary of Synthetic Simplicity: Unveiling Bias in Medical Data Augmentation, by Krishan Agyakari Raja Babu et al.
Synthetic Simplicity: Unveiling Bias in Medical Data Augmentation
by Krishan Agyakari Raja Babu, Rachana Sathish, Mrunal Pattanaik, Rahul Venkataramani
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 investigates the impact of synthetic data on downstream tasks in medical imaging and other data-scarce fields. The study reveals a critical phenomenon called simplicity bias, where neural networks exploit spurious distinctions between real and synthetic data to perform well during training but poorly during deployment when the correlation is absent. The researchers demonstrate this vulnerability through principled experiments on digit classification and cardiac view classification tasks in echocardiograms. They show that models overly rely on superficial features rather than genuine task-related complexities, compromising performance when the data source is changed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how using synthetic data to train artificial intelligence models can actually make them worse at doing their job. The problem is called simplicity bias and it happens when models learn to recognize patterns in the data that aren’t really important for the task they’re supposed to do. This means that if you use the model with real data, it might not work very well. The researchers tested this by training a model on synthetic data and then seeing how it did on real data. They found that the model was much better at recognizing digits when it was trained on synthetic data than when it was trained on real data. This is important to know because as we use more and more synthetic data to train AI models, we need to make sure they’re not falling into this trap. |
Keywords
* Artificial intelligence * Classification * Synthetic data
