Gradient Centralization: A New Optimization Technique for Deep Neural Networks

TL;DR

Gradient Centralization (GC) is a novel optimization technique that operates directly on gradients by centralizing them, leading to improved training stability, regularization, and generalization in deep neural networks across various tasks.

Contribution

This paper introduces Gradient Centralization, a simple yet effective gradient operation that enhances DNN training by improving regularization and stability, and can be integrated into existing optimizers.

Findings

GC improves training stability and convergence.

GC enhances generalization performance across tasks.

GC is easy to implement and compatible with existing optimizers.

Abstract

Optimization techniques are of great importance to effectively and efficiently train a deep neural network (DNN). It has been shown that using the first and second order statistics (e.g., mean and variance) to perform Z-score standardization on network activations or weight vectors, such as batch normalization (BN) and weight standardization (WS), can improve the training performance. Different from these existing methods that mostly operate on activations or weights, we present a new optimization technique, namely gradient centralization (GC), which operates directly on gradients by centralizing the gradient vectors to have zero mean. GC can be viewed as a projected gradient descent method with a constrained loss function. We show that GC can regularize both the weight space and output feature space so that it can boost the generalization performance of DNNs. Moreover, GC improves the Lipschitzness of the loss function and its gradient so that the training process becomes more efficient and stable. GC is very simple to implement and can be easily embedded into existing gradient based DNN optimizers with only one line of code. It can also be directly used to fine-tune the pre-trained DNNs. Our experiments on various applications, including general image classification, fine-grained image classification, detection and segmentation, demonstrate that GC can consistently improve the performance of DNN learning. The code of GC can be found at https://github.com/Yonghongwei/Gradient-Centralization.