Regularizing Class-wise Predictions via Self-knowledge Distillation (2003.13964v2)

Abstract: Deep neural networks with millions of parameters may suffer from poor generalization due to overfitting. To mitigate the issue, we propose a new regularization method that penalizes the predictive distribution between similar samples. In particular, we distill the predictive distribution between different samples of the same label during training. This results in regularizing the dark knowledge (i.e., the knowledge on wrong predictions) of a single network (i.e., a self-knowledge distillation) by forcing it to produce more meaningful and consistent predictions in a class-wise manner. Consequently, it mitigates overconfident predictions and reduces intra-class variations. Our experimental results on various image classification tasks demonstrate that the simple yet powerful method can significantly improve not only the generalization ability but also the calibration performance of modern convolutional neural networks.

Overview of "Regularizing Class-wise Predictions via Self-knowledge Distillation"

The paper "Regularizing Class-wise Predictions via Self-knowledge Distillation" presents an innovative method to enhance the generalization performance of deep neural networks (DNNs) through a novel regularization technique, which they refer to as Class-wise Self-Knowledge Distillation (CS-KD). This work contributes to the ongoing effort in machine learning to mitigate the overfitting challenges associated with the training of DNNs with millions of parameters on large datasets. The primary aim is to regularize the prediction distribution in a manner that efficiently utilizes the "dark knowledge," i.e., the confidence in incorrect predictions, to improve model robustness and calibration.

Key Contributions

1. Introduction of Class-wise Regularization: The authors propose an approach that matches the predictive distributions between different samples of the same class. Unlike traditional knowledge distillation methods that transfer knowledge from a teacher network to a student network, their method focuses on self-regularization; this applies the concept of distillation within a single network.
2. Implementation of Class-wise Self-knowledge Distillation (CS-KD): CS-KD utilizes Kullback-Leibler divergence to align the predictive outputs for samples of the same class, thereby promoting consistency. This reduces intra-class variations and prevents overconfident predictions, which are common pitfalls in deep learning models.
3. Experimental Validation: The efficacy of CS-KD is validated across several datasets, including CIFAR-100, TinyImageNet, and ImageNet, using architectures such as ResNet and DenseNet. The authors demonstrate significant improvements in top-1 error rates and calibration performance compared to the cross-entropy baseline and other regularization techniques.
4. Compatibility with Other Techniques: The paper shows that CS-KD is compatible with existing techniques like Mixup and knowledge distillation from teacher models, offering compounded benefits in network training. For instance, CS-KD combined with Mixup dramatically improves performance on the CUB-200-2011 dataset.

Implications and Future Directions

The implications of CS-KD are significant in the field of AI, particularly in enhancing the robustness and reliability of models deployed in critical applications. By reducing intra-class variations and enhancing prediction calibration, CS-KD aligns with needs in domains where prediction accuracy and reliable confidence estimates are crucial, such as autonomous driving and medical diagnosis.

The paper also paves the way for further exploration into self-distillation techniques where a model can iteratively refine its predictions based on its own feedback, possibly leading to more autonomous learning paradigms. Moreover, the integration of CS-KD with semi-supervised learning approaches could be a promising avenue, suggesting potential enhancements in scenarios with limited labeled data.

Conclusion

"Regularizing Class-wise Predictions via Self-knowledge Distillation" provides a valuable contribution to the machine learning community by addressing overfitting in DNNs through an elegant internal regularization mechanism. The method's simplicity, effectiveness, and compatibility with established techniques underscore its potential applicability across a broad range of machine learning tasks. As the field progresses, further research inspired by this work could revolutionize the way models balance generalization and confidence. The paper stands as a noteworthy step towards more intelligent and reliable artificial intelligence systems.