A Bregman Learning Framework for Sparse Neural Networks
This work addresses the need for efficient and sparse neural network training, which is crucial for resource-constrained applications, though it appears incremental as it builds on existing optimization methods like mirror descent.
The authors tackled the problem of training sparse neural networks by proposing a Bregman learning framework that starts with few parameters and adds significant ones, achieving 90.2% test accuracy on CIFAR-10 using only 3.4% of ResNet-18's parameters compared to 93.6% for the dense network.
We propose a learning framework based on stochastic Bregman iterations, also known as mirror descent, to train sparse neural networks with an inverse scale space approach. We derive a baseline algorithm called LinBreg, an accelerated version using momentum, and AdaBreg, which is a Bregmanized generalization of the Adam algorithm. In contrast to established methods for sparse training the proposed family of algorithms constitutes a regrowth strategy for neural networks that is solely optimization-based without additional heuristics. Our Bregman learning framework starts the training with very few initial parameters, successively adding only significant ones to obtain a sparse and expressive network. The proposed approach is extremely easy and efficient, yet supported by the rich mathematical theory of inverse scale space methods. We derive a statistically profound sparse parameter initialization strategy and provide a rigorous stochastic convergence analysis of the loss decay and additional convergence proofs in the convex regime. Using only 3.4% of the parameters of ResNet-18 we achieve 90.2% test accuracy on CIFAR-10, compared to 93.6% using the dense network. Our algorithm also unveils an autoencoder architecture for a denoising task. The proposed framework also has a huge potential for integrating sparse backpropagation and resource-friendly training.