Summary of Be Persistent: Towards a Unified Solution For Mitigating Shortcuts in Deep Learning, by Hadi M. Dolatabadi et al.
Be Persistent: Towards a Unified Solution for Mitigating Shortcuts in Deep Learning
by Hadi M. Dolatabadi, Sarah M. Erfani, Christopher Leckie
First submitted to arxiv on: 17 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research paper proposes a unified approach to address the issue of shortcut learning in deep neural networks (DNNs). Shortcut learning refers to the phenomenon where DNNs tend to draw inconclusive relationships between their inputs and outputs, rather than learning the intended task. This problem is widespread among various failure cases of neural networks, including generalizability issues, domain shift, adversarial vulnerability, and bias towards majority groups. The authors argue that recent advances in topological data analysis (TDA) can be leveraged to develop a unified solution for shortcut learning. They outline the key concepts of TDA, particularly persistent homology (PH), and demonstrate their approach using two case studies: unlearnable examples and bias in decision-making. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep neural networks have a problem! Instead of learning what they’re supposed to do, they often just make up random relationships between inputs and outputs. This is called “shortcut learning” and it’s really bad because it makes DNNs not work well in lots of situations. The researchers think that if we can figure out how to stop this from happening, we’ll be able to solve a lot of other problems too, like when computers get confused about what they’re seeing or when they make unfair decisions. They propose using a new way of analyzing data called topological data analysis (TDA) to find the solution. |
