Contrastive Estimation

Contrastive estimation is a powerful technique for learning unnormalized probability distributions, primarily by contrasting positive and negative data samples to estimate model parameters without explicitly calculating intractable normalization constants. Current research focuses on refining contrastive loss functions, such as InfoNCE and its variants, and exploring their application within various model architectures, including energy-based models and conditional neural processes, for tasks ranging from image classification to speech synthesis and even adapting large language models. These advancements are significantly impacting fields like self-supervised learning, generative modeling, and efficient model adaptation, offering improved performance and reduced computational costs.