Negative Sampling

Negative sampling is a crucial technique in machine learning used to improve the efficiency and effectiveness of training models, particularly those employing contrastive loss functions. Current research focuses on developing sophisticated negative sampling strategies that go beyond simple random sampling, including methods that leverage graph structures, incorporate importance sampling, and dynamically adapt to the model's learning process. These advancements are impacting various fields, from knowledge graph completion and recommendation systems to image and text retrieval, by enabling more accurate and scalable training of complex models. The ultimate goal is to generate high-quality negative samples that effectively contrast with positive samples, leading to improved model performance and robustness.