  An ideal learned representation should display transferability and
robustness. Supervised contrastive learning (SupCon) is a promising method for
training accurate models, but produces representations that do not capture
these properties due to class collapse -- when all points in a class map to the
same representation. Recent work suggests that "spreading out" these
representations improves them, but the precise mechanism is poorly understood.
We argue that creating spread alone is insufficient for better representations,
since spread is invariant to permutations within classes. Instead, both the
correct degree of spread and a mechanism for breaking this invariance are
necessary. We first prove that adding a weighted class-conditional InfoNCE loss
to SupCon controls the degree of spread. Next, we study three mechanisms to
break permutation invariance: using a constrained encoder, adding a
class-conditional autoencoder, and using data augmentation. We show that the
latter two encourage clustering of latent subclasses under more realistic
conditions than the former. Using these insights, we show that adding a
properly-weighted class-conditional InfoNCE loss and a class-conditional
autoencoder to SupCon achieves 11.1 points of lift on coarse-to-fine transfer
across 5 standard datasets and 4.7 points on worst-group robustness on 3
datasets, setting state-of-the-art on CelebA by 11.5 points.

Machine Learning;Machine Learning