Negative Data Augmentation

Negative data augmentation (NDA) enhances machine learning model training by strategically introducing synthetic "negative" samples – examples that are intentionally dissimilar to the true data distribution. Current research focuses on developing sophisticated methods for generating these negative samples, often leveraging techniques like self-supervised learning, contrastive learning, and saliency maps to ensure the generated data is both diverse and semantically relevant to the task. This approach improves model robustness, particularly in scenarios with limited training data or challenging data distributions, leading to advancements in diverse applications such as image anomaly detection, voice conversion, and small target recognition.