SSL Algorithm

Self-supervised learning (SSL) aims to train machine learning models using unlabeled data by designing pretext tasks that force the model to learn useful representations. Current research focuses on improving SSL's effectiveness across diverse domains, including speech processing (e.g., using enhanced architectures like AASIST3 for deepfake detection), graph data, and various image and signal processing applications (e.g., leveraging temporal information in satellite imagery or WiFi signals). This approach is particularly significant for scenarios with limited labeled data, offering potential for improved model generalization and efficiency in various fields, from medical image analysis to autonomous driving. Furthermore, ongoing work addresses challenges like backdoor attacks and bias mitigation within SSL frameworks.