Papers and resources collection about interpretable/explainable transfer learning (XTL)
- Awesome Explainable/Interpretable Transfer Learning (XTL)
- Contents
- Papers
- Datasets
- Lectures and Tutorials
- Other Resources
Conference
- HIVE: Evaluating the Human Interpretability of Visual Explanations [2022 ECCV] [Github] [Project]
- B-cos Networks: Alignment is All We Need for Interpretability [2022 CVPR]
- HINT: Hierarchical Neuron Concept Explainer [2022 CVPR]
- Interpretable Part-Whole Hierarchies and Conceptual-Semantic Relationships in Neural Networks [2022 CVPR]
- Deformable_ProtoPNet_An_Interpretable_Image_Classifier_Using_Deformable_Prototypes [2022 CVPR]
- Proto2Proto: Can you recognize the car, the way I do? [2022 CVPR]
- Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentation [2022 CVPR]
- Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors [2021 AAAI]
- Interpretable Compositional Convolutional Neural Networks [2021 IJCAI]
- Graph-based High-Order Relation Discovery for Fine-grained Recognition [2021 CVPR]
- Neural Prototype Trees for Interpretable Fine-grained Image Recognition [2021 CVPR]
- XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanations [2021 CVPR]
- StyleMix: Separating Content and Style for Enhanced Data Augmentation [2021 CVPR]
- ProtoPShare: Prototypical Parts Sharing for Similarity Discovery in Interpretable Image Classification [2021 KDD]
- Towards Interpretable Deep Metric Learning with Structural Matching [2021 ICCV] [Code]
- Explaining in Style: Training a GAN to explain a classifier in StyleSpace [2021 ICCV] [Project] [Code]
- This Looks Like That: Deep Learning for Interpretable Image Recognition [2019 NIPS] [Code]
- Adaptive Activation Thresholding: Dynamic Routing Type Behavior for Interpretability in Convolutional Neural Networks [2019 ICCV] [Code]
- Interpretable and Steerable Sequence Learning via Prototypes [2019 KDD]
- Interpretable Basis Decomposition for Visual Explanation [2018 ECCV]
Journal
- Hierarchical Prototype Learning for Zero-Shot Recognition [2019 TMM]
Preprint
Workshop
- This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks [2021 ICML Workshop] [Github]
Conference
- Independent Prototype Propagation for Zero-Shot Compositionality [2021 NIPS] [Code]
- ToAlign: Task-oriented Alignment for Unsupervised Domain Adaptation [2021 NIPS] [Code]
- Visualizing Adapted Knowledge in Domain Transfer [2021 CVPR] [Code]
Journal
Preprints
- Caltech-UCSD Birds-200-2011 (CUB-200) [2011]