This research explores the integration of gradient sampling optimization techniques, particularly StochGradAdam, into the pruning process of neural networks. The main objective is to address the challenge of maintaining accuracy in pruned neural models, which is critical in resource-constrained scenarios.
Through extensive experimentation on the CIFAR-10 dataset and various residual neural architectures, the study demonstrates that gradient sampling optimization with StochGradAdam can significantly preserve accuracy during and after the pruning process compared to traditional optimization methods like Adam. The results validate the versatility and effectiveness of the proposed approach, which presents a promising direction for developing efficient neural networks without compromising performance, even in environments with limited computational resources.
The theoretical analysis explains why models trained using StochGradAdam optimizer maintain larger weight magnitudes and higher variance in weights, contributing to their robustness against pruning compared to models trained with the traditional Adam optimizer. The selective update mechanism employed by StochGradAdam effectively preserves larger weights, which enhances the model's robustness, particularly against weight pruning processes.
The experiments show that StochGradAdam consistently outperforms Adam in terms of test accuracy across different ResNet models. When models undergo a 50% reduction in parameters, the durability of StochGradAdam becomes especially apparent, with pruned ResNet-56, ResNet-110, and ResNet-152 models achieving significantly higher test accuracies compared to their counterparts trained with Adam.
These findings suggest that the adoption of advanced gradient sampling techniques can play a pivotal role in the future of neural network optimization, especially in the context of model compression. The insights from this study could inform the development of new, even more effective pruning strategies that further balance the trade-off between model size, computational efficiency, and accuracy retention.
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by Juyoung Yun at arxiv.org 04-30-2024
https://arxiv.org/pdf/2312.16020.pdfDeeper Inquiries