By leveraging techniques such as contrastive learning and predictive coding, Self-Supervised Learning algorithms can extract rich and informative representations from raw data. These learned representations can then be fine-tuned on labeled datasets to improve model performance and accuracy. One of the key benefits of Self-Supervised Learning is its ability to uncover latent patterns ... https://www.lightly.ai/