Target Sparsity

Target sparsity in neural networks focuses on creating models with fewer connections while maintaining or improving performance, primarily to reduce computational costs and memory requirements. Current research emphasizes developing methods for directly controlling the level of sparsity during training, moving beyond trial-and-error approaches, and exploring techniques like constrained optimization and novel pruning algorithms for both offline and online learning scenarios. This research is significant because it addresses the growing need for efficient deep learning models, impacting various applications from image recognition to signal processing, particularly in resource-constrained environments.