Summary of Etage: Enhanced Test Time Adaptation with Integrated Entropy and Gradient Norms For Robust Model Performance, by Afshar Shamsi et al.
ETAGE: Enhanced Test Time Adaptation with Integrated Entropy and Gradient Norms for Robust Model Performance
by Afshar Shamsi, Rejisa Becirovic, Ahmadreza Argha, Ehsan Abbasnejad, Hamid Alinejad-Rokny, Arash Mohammadi
First submitted to arxiv on: 14 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 introduces ETAGE, a refined test time adaptation (TTA) method that combines entropy minimization with gradient norms and Pseudo Label Probability Difference (PLPD). Unlike traditional TTA methods that rely on entropy as a confidence metric, ETAGE prioritizes samples that are less likely to cause instability by combining high entropy with high gradient norms out of adaptation. This approach avoids overfitting to noise often observed in previous methods. The authors demonstrate the effectiveness of ETAGE through extensive experiments on CIFAR-10-C and CIFAR-100-C datasets, showing improved performance compared to existing TTA techniques, particularly in challenging and biased scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps deep learning models deal with new test data that’s different from what they learned. The problem is that some old methods don’t work well when the data is biased or noisy. ETAGE is a new way to adapt these models by picking the most important samples to train on. It combines two ideas: one that uses how much the model changes and another that looks at how certain it is about its predictions. This helps avoid overfitting to noise, which means the model doesn’t get too good at recognizing patterns that aren’t real. The authors tested ETAGE on some famous datasets and showed that it works better than other methods in tricky situations. |
Keywords
» Artificial intelligence » Deep learning » Overfitting » Probability
