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Interpretability

74 directly classified papers

Papers per year

Papers

I-CEE: Tailoring Explanations of Image Classification Models to User Expertise AAAI 2024
Log-linear Guardedness and its Implications ACL 2023
SketchXAI: A First Look at Explainability for Human Sketches CVPR 2023
PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification CVPR 2023
Towards Better Visualizing the Decision Basis of Networks via Unfold and Conquer Attribution Guidance AAAI 2023
What the DAAM: Interpreting Stable Diffusion Using Cross Attention ACL 2023
Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification CVPR 2023
Bridging the Gap Between Model Explanations in Partially Annotated Multi-Label Classification CVPR 2023
CRAFT: Concept Recursive Activation FacTorization for Explainability CVPR 2023
DISCOVER: Making Vision Networks Interpretable via Competition and Dissection NIPS 2023
Analyzing Vision Transformers for Image Classification in Class Embedding Space NIPS 2023
Exploring Geometry of Blind Spots in Vision models NIPS 2023
Brain Dissection: fMRI-trained Networks Reveal Spatial Selectivity in the Processing of Natural Images NIPS 2023
Interpretable Part-Whole Hierarchies and Conceptual-Semantic Relationships in Neural Networks CVPR 2022
Explaining Deep Convolutional Neural Networks via Latent Visual-Semantic Filter Attention CVPR 2022
Expert-Informed, User-Centric Explanations for Machine Learning AAAI 2022
DeepVisualInsight: Time-Travelling Visualization for Spatio-Temporal Causality of Deep Classification Training AAAI 2022
Learning Logic Rules for Document-Level Relation Extraction EMNLP 2021
Do Input Gradients Highlight Discriminative Features? NIPS 2021
Towards robust vision by multi-task learning on monkey visual cortex NIPS 2021
Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation IJCNLP 2021
IA-RED$^2$: Interpretability-Aware Redundancy Reduction for Vision Transformers NIPS 2021
Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation AAAI 2021
Interpreting Multivariate Shapley Interactions in DNNs AAAI 2021
A Novel Visual Interpretability for Deep Neural Networks by Optimizing Activation Maps with Perturbation AAAI 2021