Compression Aware

Compression-aware techniques aim to optimize neural network models for efficiency without significant performance loss, addressing the growing need for deploying large models on resource-constrained devices. Current research focuses on developing compression frameworks for various architectures, including transformers and convolutional neural networks, employing methods like pruning, quantization, and knowledge distillation, often combined with advanced training algorithms such as Frank-Wolfe. This research is crucial for advancing the practical application of powerful models in areas like object tracking, natural language processing, and image processing, where computational limitations previously hindered deployment. The ultimate goal is to achieve significant reductions in model size and computational cost while maintaining or even improving accuracy.