Summary of Anyloss: Transforming Classification Metrics Into Loss Functions, by Doheon Han et al.
AnyLoss: Transforming Classification Metrics into Loss Functions
by Doheon Han, Nuno Moniz, Nitesh V Chawla
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper proposes a novel approach called AnyLoss that transforms any confusion matrix-based metric into a differentiable loss function, enabling the direct optimization of such metrics. This is achieved by approximating the confusion matrix in a differentiable form using an approximation function. The method proves the differentiability of the loss functions and demonstrates its effectiveness on various neural networks with multiple datasets, particularly excelling at handling imbalanced datasets. AnyLoss also outperforms baseline models in terms of learning speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a way to turn any confusion matrix-based metric into a loss function that can be optimized directly. This helps solve problems like imbalanced learning and reduces the need for expensive hyperparameter searches. The method uses an approximation to make the confusion matrix differentiable, and it’s tested on many neural networks with various datasets. |
Keywords
» Artificial intelligence » Confusion matrix » Hyperparameter » Loss function » Optimization
