Structure-Preserving Network Compression Via Low-Rank Induced Training Through Linear Layers Composition
This addresses the challenge of network compression for efficient deployment on devices with limited resources, presenting an incremental improvement over existing low-rank and structured pruning methods.
The paper tackles the problem of compressing deep neural networks for deployment on resource-limited devices by introducing LoRITa, a low-rank induced training method that promotes low-rankness through linear layer composition and compresses via singular value truncation, achieving competitive or state-of-the-art results in terms of FLOPs and parameters drop on datasets like MNIST, CIFAR10/100, and ImageNet.
Deep Neural Networks (DNNs) have achieved remarkable success in addressing many previously unsolvable tasks. However, the storage and computational requirements associated with DNNs pose a challenge for deploying these trained models on resource-limited devices. Therefore, a plethora of compression and pruning techniques have been proposed in recent years. Low-rank decomposition techniques are among the approaches most utilized to address this problem. Compared to post-training compression, compression-promoted training is still under-explored. In this paper, we present a theoretically-justified technique termed Low-Rank Induced Training (LoRITa), that promotes low-rankness through the composition of linear layers and compresses by using singular value truncation. This is achieved without the need to change the structure at inference time or require constrained and/or additional optimization, other than the standard weight decay regularization. Moreover, LoRITa eliminates the need to (i) initialize with pre-trained models, (ii) specify rank selection prior to training, and (iii) compute SVD in each iteration. Our experimental results (i) demonstrate the effectiveness of our approach using MNIST on Fully Connected Networks, CIFAR10 on Vision Transformers, and CIFAR10/100 and ImageNet on Convolutional Neural Networks, and (ii) illustrate that we achieve either competitive or state-of-the-art results when compared to leading structured pruning and low-rank training methods in terms of FLOPs and parameters drop. Our code is available at \url{https://github.com/XitongSystem/LoRITa/tree/main}.