Sparsity Pruning

Sparsity pruning aims to reduce the size and computational cost of large neural networks by removing less important parameters (weights or neurons) while preserving accuracy. Current research focuses on developing more effective pruning algorithms, including those informed by gradients, learned masks, and game-theoretic approaches, and applying these techniques to various architectures like transformers, convolutional neural networks, and 3D Gaussian splatting models. This work is significant because it addresses the growing need for efficient and deployable deep learning models, impacting both the scalability of training large models and the resource requirements of deploying them in real-world applications. Improved pruning techniques offer the potential for significant reductions in computational cost and memory usage without sacrificing performance.