LilNetX: Lightweight Networks with EXtreme Model Compression and Structured Sparsification
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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 model sparsity on ResNet-20 while retaining the same accuracy on the CIFAR-10 dataset as well as 35 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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