Angular Margin Contrastive

Angular margin contrastive learning is a technique enhancing the effectiveness of contrastive learning by incorporating angular margins into the loss function. This approach improves the discrimination between positive and negative data pairs, particularly addressing challenges posed by noisy or ambiguous negative samples and uneven data distributions across different classes. Current research focuses on applying this method to various multimodal learning tasks, including video-language representation, multimodal sentiment analysis, and audio representation learning, often incorporating knowledge distillation or supervised learning strategies to further boost performance. These improvements in representation learning have significant implications for downstream tasks in various fields, leading to more accurate and robust models.