Beyond Cats and Dogs: Semi-supervised Classification of fuzzy labels with overclustering

Understand

A long-standing issue with deep learning is the need for large and consistently labeled datasets.

• Although the current research in semi-supervised learning can decrease the required amount of annotated data by a factor of 10 or even more, this line of research still uses distinct classes like cats and dogs.
• However, in the real-world we often encounter problems where different experts have different opinions, thus producing fuzzy labels.
• We propose a novel framework for handling semi-supervised classifications of such fuzzy labels.