Summary of Control+shift: Generating Controllable Distribution Shifts, by Roy Friedman and Rhea Chowers
Control+Shift: Generating Controllable Distribution Shifts
by Roy Friedman, Rhea Chowers
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 proposes a novel method for generating realistic datasets with distribution shifts, which can be used to evaluate the performance of various decoder-based generative models. The approach systematically creates datasets with varying intensities of distribution shifts, allowing for a comprehensive analysis of model performance degradation. The authors use these generated datasets to evaluate the performance of commonly used networks and observe a consistent decline in performance with increasing shift intensity, even when the effect is almost imperceptible to the human eye. Additionally, they find that data augmentations do not mitigate this degradation and that stronger inductive biases can increase robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates fake datasets with different levels of change, so we can see how well AI models work when there’s a shift. They use these fake datasets to test various AI models and notice that they all get worse as the shift gets bigger, even if it’s not very noticeable to humans. They also find that adding more data or using certain techniques doesn’t help make them more robust. |
Keywords
» Artificial intelligence » Decoder
