Hard Negative Mining

Hard negative mining is a technique used to improve the performance of contrastive learning models by strategically selecting challenging negative examples during training. Current research focuses on developing more effective negative sampling strategies, including dynamic and positive-aware methods, often within the context of specific model architectures like dual encoders and transformer-based embedding models. These advancements enhance the quality of learned representations in various applications, such as information retrieval, image and video processing, and person re-identification, leading to improved accuracy and efficiency. The impact is seen across diverse fields, improving the performance of models trained on large datasets with limited labeled data.