LilNetX: Lightweight Networks with EXtreme Model Compression and Structured Sparsification

• URL: http://arxiv.org/abs/2204.02965v1
• Date: Wed, 6 Apr 2022 17:59:10 GMT
• Title: LilNetX: Lightweight Networks with EXtreme Model Compression and Structured Sparsification
• Authors: Sharath Girish and Kamal Gupta and Saurabh Singh and Abhinav Shrivastava
• Abstract summary: LilNetX is an end-to-end trainable technique for neural networks. It enables learning models with specified accuracy-rate-computation trade-off.
• Score: 36.651329027209634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce LilNetX, an end-to-end trainable technique for neural networks that enables learning models with specified accuracy-rate-computation trade-off. Prior works approach these problems one at a time and often require post-processing or multistage training which become less practical and do not scale very well for large datasets or architectures. Our method constructs a joint training objective that penalizes the self-information of network parameters in a reparameterized latent space to encourage small model size while also introducing priors to increase structured sparsity in the parameter space to reduce computation. We achieve up to 50% smaller model size and 98% model sparsity on ResNet-20 while retaining the same accuracy on the CIFAR-10 dataset as well as 35% smaller model size and 42% structured sparsity on ResNet-50 trained on ImageNet, when compared to existing state-of-the-art model compression methods. Code is available at https://github.com/Sharath-girish/LilNetX.

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