PHI-S: Distribution Balancing for Label-Free Multi-Teacher Distillation
Authors: name: Mike Ranzinger, name: Jon Barker, name: Greg Heinrich, name: Pavlo Molchanov, name: Bryan Catanzaro, name: Andrew Tao

Various visual foundation models have distinct strengths and weaknesses, both
of which can be improved through heterogeneous multi-teacher knowledge
distillation without labels, termed "agglomerative models." We build upon this
body of work by studying the effect of the teachers' activation statistics,
particularly the impact of the loss function on the resulting student model
quality. We explore a standard toolkit of statistical normalization techniques
to better align the different distributions and assess their effects. Further,
we examine the impact on downstream teacher-matching metrics, which motivates
the use of Hadamard matrices. With these matrices, we demonstrate useful
properties, showing how they can be used for isotropic standardization, where
each dimension of a multivariate distribution is standardized using the same
scale. We call this technique "PHI Standardization" (PHI-S) and empirically
demonstrate that it produces the best student model across the suite of methods
studied.