Robust Feature Representation

Robust feature representation aims to create data representations that are resilient to noise, outliers, and variations in data distribution, enabling accurate and reliable machine learning. Current research focuses on developing methods that improve robustness in various contexts, including few-shot learning, noisy labels, and multi-dataset training, often employing techniques like adversarial training, unsupervised augmentation, and adaptive weighting within algorithms such as transformers and nonnegative matrix factorization. These advancements are crucial for improving the reliability and generalizability of machine learning models across diverse applications, ranging from anomaly detection and action recognition to person re-identification and face recognition.