Interactive Classification for Deep Learning Interpretation

teaser

We present an interactive system enabling users to manipulate images to explore the robustness and sensitivity of deep learning image classifiers. Using modern web technologies to run in-browser inference, users can remove image features using inpainting algorithms to obtain new classifications in real time. This system allows users to compare and contrast what image regions humans and machine learning models use for classification.

Citation

Interactive Classification for Deep Learning Interpretation

Ángel Alexander Cabrera, Fred Hohman, Jason Lin, Duen Horng (Polo) Chau
Demo at IEEE Computer Vision and Pattern Recognition (CVPR). Salt Lake City, Utah, USA, 2018.

BibTex

@article{cabrera2018interactive, title={Interactive Classification for Deep Learning Interpretation}, author={Cabrera, Ángel Alexander and Hohman, Fred and Lin, Jason and Chau, Duen Horng}, journal={Demo, IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2018}, organization={IEEE}}