Summary of Selective Learning: Towards Robust Calibration with Dynamic Regularization, by Zongbo Han et al.
Selective Learning: Towards Robust Calibration with Dynamic Regularization
by Zongbo Han, Yifeng Yang, Changqing Zhang, Linjun Zhang, Joey Tianyi Zhou, Qinghua Hu
First submitted to arxiv on: 13 Feb 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 addresses the issue of miscalibration in deep learning, where there is a discrepancy between predicted confidence and performance. Overfitting, which results in overconfident predictions during testing, is a common cause of this problem. Existing methods aim to mitigate overfitting by adding a maximum-entropy regularizer to the objective function, but they lack clear guidance on confidence adjustment. The proposed method, Dynamic Regularization (DReg), aims to learn what should be learned during training, circumventing the confidence adjusting trade-off. DReg effectively fits in-distribution samples while applying regularization dynamically to out-of-distribution samples, resulting in a robust and calibrated model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about fixing a problem with deep learning models that can get too confident in their predictions. Sometimes, these models learn everything they’re trained on, which means they make mistakes when they’re tested. The authors of this paper came up with a new way to solve this problem by adjusting how confident the model is in its predictions. They call this method Dynamic Regularization (DReg). This helps the model be more reliable and not get too sure of itself. |
Keywords
* Artificial intelligence * Deep learning * Objective function * Overfitting * Regularization
