Representation Augmentation

Representation augmentation enhances machine learning models by artificially expanding the training data through modifications to data representations, rather than raw data. Current research focuses on applying this technique to improve various tasks, including contrastive learning, knowledge distillation, and federated learning, often leveraging generative models or specific augmentation strategies tailored to the data modality (e.g., text, time series, code). These advancements aim to address challenges like data scarcity, distribution shifts, and the limitations of existing model architectures, ultimately leading to more robust and generalizable models across diverse applications.