关键词：诊断可视化;深层管道模型;视觉模型;ML-O-示波器
摘 要：The recent success of deep learning is driving a trend towards structurally complex computer vision models that combine feature extraction with predictive elements into integrated pipelines. While some of these models have achieved breakthrough results in applications like object recognition, they are dicult to design and tune, impeding progress. We feel that visual analysis can be a powerful tool to aid iterative development of deep model pipelines. Building on feature evaluation work in the computer vision community, we introduce ML-o-scope, an interactive visualization system for exploratory analysis of convolutional neural networks, a prominent type of pipelined model. We present ML-o-scope's time-lapse engine that pro- vides views into model dynamics during training, and evaluate the system as a support for tuning large scale object-classi cation pipelines.