Hard Negative

"Hard negative" mining is a crucial technique in machine learning that focuses on improving model performance by strategically selecting challenging negative examples during training. Current research emphasizes developing effective strategies for identifying and utilizing these hard negatives, often within contrastive learning frameworks and across various model architectures, including graph neural networks and transformers. This focus on hard negatives is significant because it leads to more robust and accurate models in diverse applications, such as image retrieval, natural language processing, and medical image analysis, by forcing the model to learn more discriminative features.