Learning Parameter Sharing with Tensor Decompositions and Sparsity
Keywords: Model Compression, Parameter Sharing, Sparsity, Tensor Decomposition, Knowledge Distillation, Transfer Learning, Vision Transformers, Transformers
TL;DR: SParS, a novel sparsity-aware parameter sharing algorithm, compresses large vision transformer models by 25-40% with negligible accuracy loss by leveraging shared bases and sparse factors for efficient neuron sharing across layers.
Abstract: Large neural networks achieve remarkable performance, but their size hinders deployment on resource-constrained devices. While various compression techniques exist, parameter sharing remains relatively unexplored. This paper introduces Fine-grained Parameter Sharing (FiPS), a novel algorithm that leverages the relationship between parameter sharing, tensor decomposition, and sparsity to efficiently compress large vision transformer models. FiPS employs a shared base and sparse factors to represent shared neurons across multi-layer perception (MLP) modules. Shared parameterization is initialized via Singular Value Decomposition (SVD) and optimized by minimizing block-wise reconstruction error. Experiments demonstrate that FiPS compresses DeiT-B and Swin-L MLPs to 25-40% of their original parameter count while maintaining accuracy within one percentage point of the original models.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 949
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