Cyclical Focal Loss (2202.08978v2)

Abstract: The cross-entropy softmax loss is the primary loss function used to train deep neural networks. On the other hand, the focal loss function has been demonstrated to provide improved performance when there is an imbalance in the number of training samples in each class, such as in long-tailed datasets. In this paper, we introduce a novel cyclical focal loss and demonstrate that it is a more universal loss function than cross-entropy softmax loss or focal loss. We describe the intuition behind the cyclical focal loss and our experiments provide evidence that cyclical focal loss provides superior performance for balanced, imbalanced, or long-tailed datasets. We provide numerous experimental results for CIFAR-10/CIFAR-100, ImageNet, balanced and imbalanced 4,000 training sample versions of CIFAR-10/CIFAR-100, and ImageNet-LT and Places-LT from the Open Long-Tailed Recognition (OLTR) challenge. Implementing the cyclical focal loss function requires only a few lines of code and does not increase training time. In the spirit of reproducibility, our code is available at \url{https://github.com/lnsmith54/CFL}.

• The paper presents cyclical focal loss, a new loss function that dynamically shifts focus from easy to hard examples to enhance training efficacy.
• It introduces a weighting scheme that improves convergence and generalization while maintaining computational efficiency.
• Extensive experiments on datasets like CIFAR and ImageNet demonstrate CFL's robustness and reduced hyperparameter tuning requirements.

Cyclical Focal Loss

The paper "Cyclical Focal Loss" introduces a novel loss function designed to enhance the training efficacy of deep neural networks across a variety of dataset distributions, particularly tackling the challenge presented by class imbalance. Traditional loss functions like cross-entropy are widely employed for balanced datasets; however, focal loss has been shown to enhance performance in imbalanced scenarios by focusing more on hard-to-classify examples. This paper extends the focal loss concept by introducing cyclical focal loss, which dynamically adjusts the emphasis on easy versus hard samples during the training process in a cyclical manner.

Core Contributions and Methodology

1. Cyclical Focal Loss Definition: The work builds upon the foundation laid by traditional focal loss, which targets hard samples by reducing the loss contribution from well-classified examples through a modulating factor. Cyclical Focal Loss (CFL) takes this a step further by employing a cyclical schedule that starts with a focus on easy samples, transitions to focusing on hard samples mid-training, and then shifts back to easy samples in the final epochs. This is influenced by insights from curriculum learning, aiming to guide the neural network's learning trajectory more effectively.
2. Weighting Scheme: The cyclical loss employs a new term that increases focus on confidently classified samples based on a cyclical schedule parameterized by training epochs. This cyclical approach is argued to foster a better convergence behavior and enhance model generalization by balancing the learning dynamics over the course of training.
3. Experimental Validation: The experimental section provides comprehensive evaluations on various balanced and imbalanced datasets, including CIFAR-10, CIFAR-100, ImageNet, and challenges from the Open Long-Tailed Recognition (OLTR) using ImageNet-LT and Places-LT datasets. The results demonstrate that CFL often surpasses traditional cross-entropy and focal loss, showing especially strong performance in imbalanced and few-shot learning scenarios. The authors highlight that CFL does not increase training time complexity while potentially reducing hyperparameter tuning demands.
4. Hyperparameter Robustness: CFL introduces two additional hyperparameters over traditional focal loss. However, the experiments suggest robustness to a wide range of values for these parameters, simplifying their selection process.

Implications and Future Directions

The cyclical focal loss offers a versatile loss function capable of handling both balanced and imbalanced data distributions, addressing one of the critical limitations of focal loss, which typically compromises performance on balanced datasets. This universality suggests that CFL could be used as a standard replacement for cross-entropy and focal losses in many settings, enabling better neural network performance with minimal adjustments to the underlying training framework.

In terms of future work, further exploration into various cyclical scheduling strategies, beyond the linear approach studied here, could yield insights into even more optimized training processes. Additionally, applying such cyclical frameworks to other components of the machine learning pipeline, such as learning rate or regularization, may also enhance the efficacy and efficiency of training deep models. Leveraging cyclical patterns across these parameters could potentially lead to a more holistic and integrated approach when optimizing for diverse and challenging datasets.