Summary of Input Space Mode Connectivity in Deep Neural Networks, by Jakub Vrabel et al.
Input Space Mode Connectivity in Deep Neural Networks
by Jakub Vrabel, Ori Shem-Ur, Yaron Oz, David Krueger
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistical Mechanics (cond-mat.stat-mech); 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 extends the concept of loss landscape mode connectivity from parameter space to input space in deep neural networks. Mode connectivity refers to the existence of low-loss paths between different solutions obtained through gradient descent. The authors present theoretical and empirical evidence for this phenomenon in the input space, showing that similar input images with the same predictions are generally connected, with a simple path in trained models. They utilize real, interpolated, and synthetic inputs created using the input optimization technique for feature visualization. This research has implications for adversarial examples and detection, as well as applications for deep network interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how deep neural networks work when they’re given different inputs. It’s like a map that shows how changing one thing (an input) affects the outcome of a computation. The researchers found that similar inputs can lead to similar results, and that this is true even in untrained models. They used special techniques to create new images and test their ideas. This research could help us understand why some fake images are hard to spot, and how we can make deep networks more transparent. |
Keywords
» Artificial intelligence » Gradient descent » Optimization
