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We introduce LilNetX, an end-to-end trainable technique for neural networksthat enables learning models with specified accuracy-rate-computationtrade-off. Prior works approach these problems one at a time and often requirepost-processing or multistage training which become less practical and do notscale very well for large datasets or architectures. Our method constructs ajoint training objective that penalizes the self-information of networkparameters in a reparameterized latent space to encourage small model sizewhile also introducing priors to increase structured sparsity in the parameterspace 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-10dataset as well as 35% smaller model size and 42% structured sparsity onResNet-50 trained on ImageNet, when compared to existing state-of-the-art modelcompression methods. Code is available athttps://github.com/Sharath-girish/LilNetX.

LilNetX: Lightweight Networks with EXtreme Model Compression and Structured Sparsification