Unsupervised Learning
Unsupervised Learning in the context of Computer Vision refers to the process of teaching machines to identify and extract meaningful patterns from images without explicit human supervision or labeled data. It involves developing algorithms that can learn from the inherent structure present in unlabeled image datasets, enabling the discovery of novel insights and representations. This approach is particularly valuable when dealing with large-scale image data where manual labeling becomes impractical. Unsupervised Learning techniques in Computer Vision include dimensionality reduction, clustering, autoencoders, and generative models, all aimed at capturing the underlying relationships between images, thereby driving innovation in various applications such as object recognition, segmentation, and anomaly detection.