Meta Pruning

Meta-pruning focuses on developing efficient algorithms to automatically reduce the size and computational cost of deep neural networks while preserving accuracy. Current research explores various approaches, including leveraging optimal transport theory for improved pruning strategies and employing meta-learning techniques with reward functions to guide the pruning process, often applied to convolutional neural networks like ResNets and MobileNets. These advancements aim to address the high computational demands of large neural networks, leading to more efficient models for deployment on resource-constrained devices and accelerating training times. The ultimate goal is to create methods that automatically find optimal sparse network architectures without extensive manual tuning or retraining.