  In this work, we propose a general framework called Concept-Monitor to help
demystify the black-box DNN training processes automatically using a novel
unified embedding space and concept diversity metric. Concept-Monitor enables
human-interpretable visualization and indicators of the DNN training processes
and facilitates transparency as well as deeper understanding on how DNNs
develop along the during training. Inspired by these findings, we also propose
a new training regularizer that incentivizes hidden neurons to learn diverse
concepts, which we show to improve training performance. Finally, we apply
Concept-Monitor to conduct several case studies on different training paradigms
including adversarial training, fine-tuning and network pruning via the Lottery
Ticket Hypothesis
