ShakeDrop Regularization for Deep Residual Learning (1802.02375v3)

Abstract: Overfitting is a crucial problem in deep neural networks, even in the latest network architectures. In this paper, to relieve the overfitting effect of ResNet and its improvements (i.e., Wide ResNet, PyramidNet, and ResNeXt), we propose a new regularization method called ShakeDrop regularization. ShakeDrop is inspired by Shake-Shake, which is an effective regularization method, but can be applied to ResNeXt only. ShakeDrop is more effective than Shake-Shake and can be applied not only to ResNeXt but also ResNet, Wide ResNet, and PyramidNet. An important key is to achieve stability of training. Because effective regularization often causes unstable training, we introduce a training stabilizer, which is an unusual use of an existing regularizer. Through experiments under various conditions, we demonstrate the conditions under which ShakeDrop works well.

• The paper presents ShakeDrop, a novel stochastic regularization technique extending Shake-Shake to a wider range of deep residual networks to improve generalization.
• Experiments show ShakeDrop consistently reduces generalization errors across various ResNet architectures and datasets, outperforming traditional methods like RandomDrop.
• ShakeDrop effectively integrates with data augmentation methods like mixup, offering complementary benefits for further error reduction and suggesting potential for future stochastic regularization research.

ShakeDrop Regularization for Deep Residual Learning

In the paper, "ShakeDrop Regularization for Deep Residual Learning," the authors present a novel regularization technique designed to mitigate overfitting in deep neural networks, particularly focusing on residual networks such as ResNet, Wide ResNet, PyramidNet, and ResNeXt. The proposed method, ShakeDrop, extends the shaking mechanism previously used in Shake-Shake regularization—a technique effective with ResNeXt—to a broader range of architectures, offering improvements in generalization performance.

The ShakeDrop method employs stochastic regularization to variably perturb the network during training. It introduces randomness both in the forward and backward passes of network layers deemed for regularization, which helps it to escape potential local minima during training—a common hurdle in deep learning. This stochastic technique is influenced by probabilistic switching akin to RandomDrop (Stochastic Depth) but with the added complexity of perturbed gradients and activations controlled by random variables.

Key Insights and Results

The authors conducted extensive experiments across multiple network architectures (ResNet families) and datasets (CIFAR-10/100 and ImageNet) to validate the effectiveness of ShakeDrop. The findings offer several noteworthy insights:

• Effective Regularization: ShakeDrop consistently reduced generalization errors across various architectures, surpassing traditional methods such as Vanilla networks and RandomDrop, and even extended the applicability of Shake-Shake beyond ResNeXt.
• Parameter Sensitivity: The selection of parameters—particularly the probabilistic switch parameter $p_L$ and the ranges for random coefficients $\alpha$ and $\beta$—was crucial to achieving optimal performance. ShakeDrop showed varying sensitivity based on network depth, with deeper networks generally benefiting from lower $p_L$.
• Collaborative Use with Mixup: The authors demonstrated that ShakeDrop could integrate seamlessly with other data augmentation approaches like mixup, leading to further improvements in error reduction, emphasizing its collaborative potential rather than competitive stance.

Implications and Future Directions

The introduction of ShakeDrop extends the toolkit available for regularizing deep residual networks, positing itself as a valuable complementary approach alongside existing methods. The use of randomness in both forward and backward passes aligns well with the need for robust models that generalize better by avoiding overfitting traps.

The theoretical and practical implications of ShakeDrop could spur further development of stochastic regularization techniques, encouraging experimentation with different noise and perturbation mechanisms. Future research might explore understanding the interplay between different stochastic methods and explore their combined effects on even broader architectures and learning scenarios.

Overall, this paper contributes significantly to the domain of deep learning regularization, providing both theoretical rationale and empirical substantiation for ShakeDrop as a versatile, effective regularization strategy.