Calibrating Deep Neural Networks using Focal Loss (2002.09437v2)

Abstract: Miscalibration - a mismatch between a model's confidence and its correctness - of Deep Neural Networks (DNNs) makes their predictions hard to rely on. Ideally, we want networks to be accurate, calibrated and confident. We show that, as opposed to the standard cross-entropy loss, focal loss [Lin et. al., 2017] allows us to learn models that are already very well calibrated. When combined with temperature scaling, whilst preserving accuracy, it yields state-of-the-art calibrated models. We provide a thorough analysis of the factors causing miscalibration, and use the insights we glean from this to justify the empirically excellent performance of focal loss. To facilitate the use of focal loss in practice, we also provide a principled approach to automatically select the hyperparameter involved in the loss function. We perform extensive experiments on a variety of computer vision and NLP datasets, and with a wide variety of network architectures, and show that our approach achieves state-of-the-art calibration without compromising on accuracy in almost all cases. Code is available at https://github.com/torrvision/focal_calibration.

• The paper shows that applying focal loss mitigates DNN miscalibration by reducing overconfidence in both correct and incorrect predictions.
• It introduces an adaptive hyperparameter gamma selection method that optimizes calibration without compromising overall accuracy.
• Empirical results across diverse datasets and architectures demonstrate improved reliability and enhanced out-of-distribution detection.

An Overview of "Calibrating Deep Neural Networks using Focal Loss"

The paper "Calibrating Deep Neural Networks using Focal Loss" investigates the miscalibration issue in deep neural networks (DNNs) and offers a novel solution applying focal loss to improve calibration without compromising accuracy. The authors present a comprehensive paper on the calibration of DNNs, a critical aspect in contexts where model confidence is as important as model accuracy.

Key Contributions

1. Insight into Calibration Issues: The paper starts by establishing the problem of miscalibration in DNNs, where the predicted probabilities often do not reflect true correctness likelihood. This miscalibration is typically due to the overfitting potential of high-capacity models on commonly used negative log-likelihood (NLL) loss. The authors propose that this miscalibration is exacerbated because the NLL allows increasing prediction confidence for both correctly and incorrectly classified instances beyond necessary.
2. Focal Loss as a Solution: The focal loss, originally designed for handling imbalanced class distributions, introduces a modulation term that emphasizes hard-to-classify instances. By doing so, it inherently serves as a form of entropy regularization, increasing prediction confidence selectively and reducing overfitting on NLL. The focal loss thus creates a balance whereby models achieve accurate predictions without excessive confidence.
3. Automatic Hyperparameter Selection: A notable contribution is the introduction of a principled approach for automatically selecting the focal loss hyperparameter $\gamma$. The authors derive that adapting $\gamma$ based on current prediction confidence can further improve the model calibration, allowing the model to adjust inherently rather than relying solely on post-hoc adjustments such as temperature scaling.
4. Empirical Validation: Extensive experiments conducted on diverse datasets, including CIFAR-10, CIFAR-100, Tiny-ImageNet, and text classification problems, demonstrate that the use of focal loss leads to state-of-the-art calibration performance. These findings are robust across a variety of network architectures including ResNet, Wide-ResNet, and DenseNet, ensuring broad applicability.
5. Out-of-Distribution(DD) Detection: A particularly interesting observation is that the calibration benefits extend even when the model encounters data distribution shifts. The ability of focal loss-based models to maintain calibration when faced with out-of-distribution data represents a significant advantage over methods like temperature scaling, which typically assume an i.i.d. setting.

Implications and Future Developments

The implications of this research are substantial both in theoretical exploration and practical applications. Theoretically, the interpretation of focal loss as an implicit form of entropy regularization invites further exploration of loss functions to understand their underlying calibration properties. Practically, this approach could handle real-world implementation challenges where models must not only make accurate predictions but also provide reliable confidence scores, essential in safety-critical domains like autonomous driving or medical diagnosis.

A potential area for future development lies in further refining and generalizing the techniques for automatic hyperparameter selection. Additionally, examining the integration of focal loss with more advanced neural architectures and exploring synergistic effects with other calibration-driven adjustments could offer deeper insights and enhancements.

In summary, the paper offers a meticulous analysis of DNN calibration issues and presents focal loss as an effective mechanism for improvement. Its rigorous experimental support enhances the credibility of focal loss for broader adoption in calibrating deep learning models, setting a promising direction for subsequent research.