Eager Mode Quantization¶| pytorch.org
Binary Quantization is a newly introduced mechanism of reducing the memory footprint and increasing performance| qdrant.tech
Discover the efficiency of scalar quantization for optimized data storage and enhanced performance. Learn about its data compression benefits and efficiency improvements.| qdrant.tech
The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ones are far apart. Contrastive learning can be applied to both supervised and unsupervised settings. When working with unsupervised data, contrastive learning is one of the most powerful approaches in self-supervised learning. Contrastive Training Objectives In early versions of loss functions for contrastive learning, only one positive a...| lilianweng.github.io