Compression Method

Data compression research is actively exploring the use of neural networks, particularly transformers and convolutional autoencoders, to surpass the performance of traditional algorithms like gzip and JPEG. Current efforts focus on improving compression ratios and speed, especially for large language models and high-dimensional data like images and videos, often employing techniques such as quantization, pruning, and knowledge distillation. These advancements are crucial for managing the ever-increasing volume of data in various fields, from scientific computing and medical imaging to natural language processing, enabling efficient storage, transmission, and processing.