Contrastive learning has emerged as a pivotal technique in unsupervised representation learning, fundamentally transforming how models learn to encode data without explicit supervision. At its core, contrastive learning aims to learn representations by contrasting positive pairs against negative pairs, thereby ensuring that similar instances are closer in the latent space while dissimilar ones are farther