Self Supervised Signal

Self-supervised signal learning aims to train machine learning models without relying on extensive labeled datasets, leveraging inherent data structures or transformations as learning signals. Current research focuses on developing novel self-supervised signals, including contrastive methods, entropy bottleneck approaches, and techniques based on feature propagation or consistency across multiple views or model augmentations. This approach is significant because it addresses the limitations of supervised learning, particularly the high cost and potential biases associated with data annotation, enabling the development of more robust and efficient models across various applications, such as image recognition, video segmentation, and anomaly detection.