Contrastive Learning
SimSiam simplifies Self-Supervised Learning by eliminating the need for negative samples and momentum encoders. Using a dual-branch Siamese network and a stop-gradient mechanism, it prevents representation collapse while achieving competitive
Supervised Learning has been dominant for years, but its reliance on labeled data—a costly and time-consuming resource—creates challenges, especially in areas like medical imaging. On the other hand, Unsupervised Learning,
