Unsupervised Image Classification

Unsupervised Image Classification refers to the task of automatically clustering an image dataset into its true categories without labeled data. This task aims to learn from unlabeled image data, discover the inherent structure and patterns within the data, and thereby achieve image classification. Not only can this significantly reduce annotation costs, but it also enhances the model's adaptability to large-scale unlabeled data, making it highly valuable for various applications.