Deep neural networks (DNNs) have been widely used for decision making, prompting a surge of interest in interpreting how these complex models work. Recent literature on DNN interpretation has revolved around already-trained models; however, much less research focuses on interpreting how the models evolve as they are trained. Interpreting model evolution is crucial to monitor network training and can aid proactive decisions about necessary interventions. In this work, we present ConceptEvo, a general interpretation framework for DNNs that reveals the inception and evolution of detected concepts during training. Through a large-scale human evaluation with 260 participants and quantitative experiments, we show that ConceptEvo discovers evolution across different models that are meaningful to humans, helpful for early-training intervention decisions, and crucial to the prediction for a given class.
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