Adaptive Gradient Regularization: A Faster and Generalizable Optimization Technique for Deep Neural Networks (2407.16944v4)

Abstract: Stochastic optimization plays a crucial role in the advancement of deep learning technologies. Over the decades, significant effort has been dedicated to improving the training efficiency and robustness of deep neural networks, via various strategies including gradient normalization (GN) and gradient centralization (GC). Nevertheless, to the best of our knowledge, no one has considered to capture the optimal gradient descent trajectory, by adaptively controlling gradient descent direction. To address this concern, this paper is the first attempt to study a new optimization technique for deep neural networks, using the sum normalization of a gradient vector as coefficients, to dynamically regularize gradients and thus to effectively control optimization direction. The proposed technique is hence named as the adaptive gradient regularization (AGR). It can be viewed as an adaptive gradient clipping method. The theoretical analysis reveals that the AGR can effectively smooth the loss landscape, and hence can significantly improve the training efficiency and model generalization performance. We note that AGR can greatly improve the training efficiency of vanilla optimizers' including Adan and AdamW, by adding only three lines of code. The final experiments conducted on image generation, image classification, and language representation, demonstrate that the AGR method can not only improve the training efficiency but also enhance the model generalization performance.

• The paper introduces AGR, which adaptively normalizes gradient vectors to improve training stability and overall performance.
• AGR dynamically scales gradients, outperforming traditional clipping with better FID scores and accuracy across image and NLP datasets.
• The method integrates seamlessly with optimizers like AdamW, providing a practical boost to model generalization in diverse deep learning applications.

An Adaptive Gradient Regularization Method

In the field of neural network optimization, the proposed paper presents an adaptive gradient regularization (AGR) method with promising implications for improving training stability and generalization performance. This paper by Jiang, Bao, and Si introduces a novel optimization technique that leverages the gradient's magnitude to implement adaptive regularization. The method modifies gradient vectors across all dimensions, facilitating a smoother learning process by dynamically adjusting the learning rate based on gradient behavior.

Key Contributions

The introduction of AGR as an adaptive gradient clipping method represents a significant enhancement over traditional gradient clipping techniques, which lack dynamic adaptability. Unlike rigid clipping thresholds that can impede optimization, AGR's coefficient vector approach normalizes gradients dynamically, subtracting a product that scales the learned coefficients by the vanilla gradient. The authors provide salient insights into AGR's theoretical underpinnings, establishing its operation as a regularization method that inherently scales the learning rate adaptively. This theoretical groundwork is critical, as it implies that AGR can be seamlessly integrated with existing optimizers like AdamW and Adan using minimal code.

Experimental Validation and Results

Through extensive experimental evaluation in domains such as image generation, image classification, and natural language processing, AGR has demonstrated its efficacy in outperforming several state-of-the-art techniques. For instance, in the context of image generation using the DDPM model on the CIFAR10 dataset, AGR-integrated optimizers exhibited improved FID scores and IS scores, indicating enhanced quality and diversity of generated images. Similarly, AGR showed a notable performance boost in image classification tasks across various architectures including ResNet, VGG, and transformer-based models on datasets like CIFAR100 and Tiny-ImageNet.

Moreover, the incorporation of AGR in NLP tasks using the ALBERT model showcased improvements in accuracy on the WikiText-2 dataset, affirming its versatility across diverse machine learning applications. One of the striking observations was the improved generalization and training stability—a consequence supported by the theoretical analysis in the paper highlighting AGR's role in stabilizing loss landscapes and adapting learning rate dynamics.

Theoretical Implications and Future Prospects

The theoretical implications of AGR extend beyond its immediate application. With its roots in gradient magnitude adaptation, AGR contributes to the broader understanding of dynamic optimization strategies in deep learning. Its methodology suggests potential applicability in more granular learning systems, where adaptability directly influences model convergence rates and the exploration of weight space.

Moving forward, exploring the adaptability of AGR across different neural network paradigms beyond the used architectures remains an open avenue. Future research might further investigate the influence of AGR on the robustness of models facing adversarial inputs and its integration into more comprehensive learning systems.

In summary, the adaptive gradient regularization method presented in this work underscores the importance of dynamically adjusted optimization techniques in contemporary machine learning research. Through theoretical insights and empirical validation, this method enriches the set of tools available to researchers and practitioners aiming to refine neural network performance across a spectrum of tasks.