| Paper ID | IVMSP-10.6 |
| Paper Title |
VISUALIZING ASSOCIATION IN EXEMPLAR-BASED CLASSIFICATION |
| Authors |
Taiga Kashima, Ryuichiro Hataya, Hideki Nakayama, University of Tokyo, Japan |
| Session | IVMSP-10: Metric Learning and Interpretability |
| Location | Gather.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 |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
