Reviews: Knowledge Distillation by On-the-Fly Native Ensemble

Summary: Authors propose a novel multi-branch network with a loss function that uses distillation from a combined branch to distill into individual branches. The technique is motivated by the idea that Teacher-Student knowledge distillation is a two-step process often requiring a large pre-trained teacher. Their method builds a teacher, out of weighted ensemble and uses that to train the network. They are able to show that the combined network (ONE-E) is far superior to standalone networks, and the individual branch (ONE) is also better than its counterpart (i.e if it were trained without any of the loss functions and the branches). Pros: 1. Excellent write-up This is a very well written paper.