Quantization Learning

Quantization learning focuses on representing data using fewer bits, thereby reducing computational costs and memory requirements for machine learning models, particularly in resource-constrained environments. Current research explores various quantization techniques, including asymmetric quantization with learnable parameters, and their integration with different model architectures like transformers and convolutional neural networks, often within self-supervised or federated learning frameworks. This research is significant for improving the efficiency and scalability of machine learning across diverse applications, from image compression and retrieval to edge computing and personalized federated learning.