Generative Self Supervised

Generative self-supervised learning aims to learn robust data representations without explicit labels by training models to reconstruct or generate data samples. Current research focuses on adapting this approach to various data types, including graphs (using graph autoencoders and variations), hypergraphs, and time-series data like fMRI scans, often addressing challenges like class imbalance and preserving crucial data features. These methods show promise in improving performance on downstream tasks such as node classification, anomaly detection (e.g., in medical applications like Parkinson's gait analysis), and sentence representation learning, particularly where labeled data is scarce or expensive to obtain.