Paper:
APPRENTICE: USING KNOWLEDGE DISTILLATION TECHNIQUES TO IMPROVE LOW-PRECISION NET- WORK ACCURACY
Deep learning networks have achieved state-of-the-art accuracies on computer vision workloads like image classification and object detection. The performant systems, however, typically involve big models with numerous parameters. Once trained, a challenging aspect for such top performing models is deployment on resource constrained inference systems -the models (often deep networks or wide networks or both) are compute and memory intensive. Low-precision numerics and model compression using knowledge distillation are popular techniques to lower both the compute requirements and memory footprint of these deployed models. In this paper, we study combination of these two techniques and show that the performance of low-precision networks can be significantly improved by using knowledge distillation techniques. Our approach, Apprentice, achieves state-of-the-art accuracies using ternary precision and 4-bit precision for variants of ResNet architecture on ImageNet dataset. We present three schemes using which one can apply knowledge distillation techniques to various stages of the train-and-deploy pipeline.

Review:
The authors investigate knowledge distillation as a way to learn low precision networks. They propose three training schemes to train a low precision student network from a teacher network. They conduct experiments on ImageNet-1k with variants of ResNets and multiple low precision regimes and compare performance with previous works
Pros:
(+) The paper is well written, the schemes are well explained
(+) Ablations are thorough and comparisons are fair
Cons:
(-) The gap with full precision models is still large 
(-) Transferability of the learned low precision models to other tasks is not discussed
The authors tackle a very important problem, the one of learning low precision models without comprosiming performance. For scheme-A, the authors show the performance of the student network under many low precision regimes and different depths of teacher networks. One observation not discussed by the authors is that the performance of the student network under each low precision regime doesn't improve with deeper teacher networks (see Table 1, 2 & 3). As a matter of fact, under some scenarios performance even decreases. 
The authors do not discuss the gains of their best low-precision regime in terms of computation and memory.
Finally, the true applications for models with a low memory footprint are not necessarily related to image classification models (e.g. ImageNet-1k). How good are the low-precision models trained by the authors at transferring to other tasks? Is it possible to transfer student-teacher training practices to other tasks?