Forget the Data and Fine-Tuning! Just Fold the Network to Compress

Dong Wang, Haris Šikić, Lothar Thiele, Olga Saukh

Keywords: Model compression, model folding, model merging

TL;DR: Model Folding: Compressing Deep Networks Without Data or Fine-Tuning

Abstract:

We introduce model folding, a novel data-free model compression technique that merges structurally similar neurons across layers, significantly reducing the model size without the need for fine-tuning or access to training data. Unlike existing methods, model folding preserves data statistics during compression by leveraging k-means clustering, and using novel data-free techniques to prevent variance collapse or explosion. Our theoretical framework and experiments across standard benchmarks, including ResNet18 and LLaMA-7B, demonstrate that model folding achieves comparable performance to data-driven compression techniques and outperforms recently proposed data-free methods, especially at high sparsity levels. This approach is particularly effective for compressing large-scale models, making it suitable for deployment in resource-constrained environments.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 6387