Paper at the Visual Analytics for Deep Learning workshop at VAST

Deep models are at the heart of computer vision research recently. With a significant performance boost over conventional approaches, it is relatively easy to treat them like black boxes and enjoy the benefits they offer. However, if we are to improve and develop them further, understanding their reasoning process is key.

Motivated by making the understanding process effortless both for the scientists who develop these models and the professionals using them, in this paper, we present an interactive plug&play web based deep learning visualization system. Our system allows users to upload their trained models and visualize the maximum activations of specific units or create attention/saliency maps over their input. It operates on top of most popular deep learning frameworks and is platform independent due to its web based implementation. We demonstrate the practical aspects of our two main features MaxOut and Reason through visualizations on models trained with artistic paintings from the OmniArt dataset and elaborate on the results.

Plug-and-Play Interactive Deep Network Visualization.

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