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Interpretability

74 directly classified papers

Papers per year

Papers

Explainable Models with Consistent Interpretations AAAI 2021
Enhanced Regularizers for Attributional Robustness AAAI 2021
CAMERAS: Enhanced Resolution and Sanity Preserving Class Activation Mapping for Image Saliency CVPR 2021
Relevance-CAM: Your Model Already Knows Where To Look CVPR 2021
Enriching ImageNet With Human Similarity Judgments and Psychological Embeddings CVPR 2021
There and Back Again: Revisiting Backpropagation Saliency Methods CVPR 2020
Attack to Explain Deep Representation CVPR 2020
Scalable Uncertainty for Computer Vision With Functional Variational Inference CVPR 2020
Attribution in Scale and Space CVPR 2020
Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning NIPS 2020
A Diagnostic Study of Explainability Techniques for Text Classification EMNLP 2020
Towards Visually Explaining Variational Autoencoders CVPR 2020
Attention Branch Network: Learning of Attention Mechanism for Visual Explanation CVPR 2019
Classifier-Agnostic Saliency Map Extraction AAAI 2019
Deliberative Explanations: visualizing network insecurities NIPS 2019
Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks CVPR 2019
Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules? EMNLP 2018
Excitation Backprop for RNNs CVPR 2018
Interpretable Convolutional Neural Networks CVPR 2018
Interpret Neural Networks by Identifying Critical Data Routing Paths CVPR 2018
Improving Interpretability of Deep Neural Networks With Semantic Information CVPR 2017
Inverting Visual Representations With Convolutional Networks CVPR 2016
Understanding Deep Image Representations by Inverting Them CVPR 2015
Object Classification with Adaptable Regions CVPR 2014