Analyzing and Improving Representations with the Soft Nearest Neighbor Loss

Understand

We explore and expand the $\textit{Soft Nearest Neighbor Loss}$ to measure the $\textit{entanglement}$ of class manifolds in representation space: i.e., how close pairs of points from the same class are relative to pairs of points from different classes.

• We demonstrate several use cases of the loss.
• As an analytical tool, it provides insights into the evolution of class similarity structures during learning.
• Surprisingly, we find that $\textit{maximizing}$ the entanglement of representations of different classes in the hidden layers is beneficial for discrimination in the final layer, possibly because it encourages representations to identify class-independent similarity structures.