Conference paper
Demo paper
Deepling: A Visual Interpretability System for Convolutional Neural Networks
Abstract
We demonstrate an interactive visualization system to promote interpretability of convolutional neural networks (CNNs). Interpretation of deep learning models acts on the interface between increasingly complex model architectures and model architects, to provide an understanding of how a model operates, where it fails, or why it succeeds. Based on preliminary expert interviews and a careful literature review we design the system to comprehensively support architects on 4 visual dimensions.
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