2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIVMSP-10.6
Paper Title VISUALIZING ASSOCIATION IN EXEMPLAR-BASED CLASSIFICATION
Authors Taiga Kashima, Ryuichiro Hataya, Hideki Nakayama, University of Tokyo, Japan
SessionIVMSP-10: Metric Learning and Interpretability
LocationGather.Town
Session Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVARS] Image & Video Analysis, Synthesis, and Retrieval
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Recent progress in deep learning has enhanced image classification performance. However, classification using deep convolutional neural networks lacks interpretability. To solve this problem, we propose a novel method of explainable classification; this method uses images representing each image class, which we call exemplars. Our method comprises encoder-decoder models (association networks) and a classifier. First, the association networks transform each input image into an image that a deep neural network associates, which we call an associative image. Then, the image-level similarity between the associative images and the exemplars is used as a feature for classification. This similarity explains the decision of the classifiers. We conducted experiments using CIFAR-10, CIFAR-100, and STL-10 and demonstrated our classifier's interpretability through the proposed visualization technique.