An Adaptive Tensor-Train Decomposition Approach for Efficient Deep Neural Network Compression

• URL: http://arxiv.org/abs/2408.01534v3
• Date: Tue, 16 Sep 2025 01:07:49 GMT
• Title: An Adaptive Tensor-Train Decomposition Approach for Efficient Deep Neural Network Compression
• Authors: Shiyi Luo, Mingshuo Liu, Yifeng Yu, Shangping Ren, Yu Bai,
• Abstract summary: We introduce a novel, automatic, and budget-aware rank selection method for efficient model compression.<n> Layer-Wise Imprinting Quantitation (LWIQ) quantifies each layer's significance within a neural network by integrating a proxy classifier.<n> Experimental results on the CIFAR-10 dataset show that our LWIQ improved by 63.2% in rank search efficiency.
• Score: 4.869958813908807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the field of model compression, choosing an appropriate rank for tensor decomposition is pivotal for balancing model compression rate and efficiency. However, this selection, whether done manually or through optimization-based automatic methods, often increases computational complexity. Manual rank selection lacks efficiency and scalability, often requiring extensive trial-and-error, while optimization-based automatic methods significantly increase the computational burden. To address this, we introduce a novel, automatic, and budget-aware rank selection method for efficient model compression, which employs Layer-Wise Imprinting Quantitation (LWIQ). LWIQ quantifies each layer's significance within a neural network by integrating a proxy classifier. This classifier assesses the layer's impact on overall model performance, allowing for a more informed adjustment of tensor rank. Furthermore, our approach includes a scaling factor to cater to varying computational budget constraints. This budget awareness eliminates the need for repetitive rank recalculations for different budget scenarios. Experimental results on the CIFAR-10 dataset show that our LWIQ improved by 63.2% in rank search efficiency, and the accuracy only dropped by 0.86% with 3.2x less model size on the ResNet-56 model as compared to the state-of-the-art proxy-based automatic tensor rank selection method.

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