Wasserstein Distances Made Explainable: Insights Into Dataset Shifts and Transport Phenomena
Fuente:
arXiv
Saved in:
| Main Authors: | Naumann, Philip, Kauffmann, Jacob, Montavon, Grégoire |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Reliable Modeling of Distribution Shifts via Displacement-Reshaped Optimal Transport
by: Naumann, Philip, et al.
Published: (2026)
by: Naumann, Philip, et al.
Published: (2026)
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces
by: Chormai, Pattarawat, et al.
Published: (2022)
by: Chormai, Pattarawat, et al.
Published: (2022)
Stereographic Spherical Sliced Wasserstein Distances
by: Tran, Huy, et al.
Published: (2024)
by: Tran, Huy, et al.
Published: (2024)
Dataset Distillation via the Wasserstein Metric
by: Liu, Haoyang, et al.
Published: (2023)
by: Liu, Haoyang, et al.
Published: (2023)
Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation
by: Lv, Jiaming, et al.
Published: (2024)
by: Lv, Jiaming, et al.
Published: (2024)
Drainage: A Unifying Framework for Addressing Class Uncertainty
by: Taha, Yasser, et al.
Published: (2025)
by: Taha, Yasser, et al.
Published: (2025)
Fast and Accurate Explanations of Distance-Based Classifiers by Uncovering Latent Explanatory Structures
by: Bley, Florian, et al.
Published: (2025)
by: Bley, Florian, et al.
Published: (2025)
Interpreting CLIP: Insights on the Robustness to ImageNet Distribution Shifts
by: Crabbé, Jonathan, et al.
Published: (2023)
by: Crabbé, Jonathan, et al.
Published: (2023)
Advancing Histopathology-Based Breast Cancer Diagnosis: Insights into Multi-Modality and Explainability
by: Abdullakutty, Faseela, et al.
Published: (2024)
by: Abdullakutty, Faseela, et al.
Published: (2024)
deepTerra -- AI Land Classification Made Easy
by: Wilkinson, Andrew Keith
Published: (2025)
by: Wilkinson, Andrew Keith
Published: (2025)
Bispectral OT: Dataset Comparison using Symmetry-Aware Optimal Transport
by: Ma, Annabel, et al.
Published: (2025)
by: Ma, Annabel, et al.
Published: (2025)
Orthogonal Finetuning Made Scalable
by: Qiu, Zeju, et al.
Published: (2025)
by: Qiu, Zeju, et al.
Published: (2025)
Video Understanding by Design: How Datasets Shape Architectures and Insights
by: Wang, Lei, et al.
Published: (2025)
by: Wang, Lei, et al.
Published: (2025)
Sliced Wasserstein with Random-Path Projecting Directions
by: Nguyen, Khai, et al.
Published: (2024)
by: Nguyen, Khai, et al.
Published: (2024)
Do you see what I see? An Ambiguous Optical Illusion Dataset exposing limitations of Explainable AI
by: Newen, Carina, et al.
Published: (2025)
by: Newen, Carina, et al.
Published: (2025)
Insights from the Use of Previously Unseen Neural Architecture Search Datasets
by: Geada, Rob, et al.
Published: (2024)
by: Geada, Rob, et al.
Published: (2024)
Towards Desiderata-Driven Design of Visual Counterfactual Explainers
by: Bender, Sidney, et al.
Published: (2025)
by: Bender, Sidney, et al.
Published: (2025)
Label Dropout: Improved Deep Learning Echocardiography Segmentation Using Multiple Datasets With Domain Shift and Partial Labelling
by: Islam, Iman, et al.
Published: (2024)
by: Islam, Iman, et al.
Published: (2024)
Blind Inverse Problem Solving Made Easy by Text-to-Image Latent Diffusion
by: Dontas, Michail, et al.
Published: (2024)
by: Dontas, Michail, et al.
Published: (2024)
Learning Discrete Autoregressive Priors with Wasserstein Gradient Flow
by: Zheng, Bowen, et al.
Published: (2026)
by: Zheng, Bowen, et al.
Published: (2026)
Semi-Supervised Image Captioning Considering Wasserstein Graph Matching
by: Yang, Yang
Published: (2024)
by: Yang, Yang
Published: (2024)
GeONet: a neural operator for learning the Wasserstein geodesic
by: Gracyk, Andrew, et al.
Published: (2022)
by: Gracyk, Andrew, et al.
Published: (2022)
Online Reward-Weighted Fine-Tuning of Flow Matching with Wasserstein Regularization
by: Fan, Jiajun, et al.
Published: (2025)
by: Fan, Jiajun, et al.
Published: (2025)
Expanding on the BRIAR Dataset: A Comprehensive Whole Body Biometric Recognition Resource at Extreme Distances and Real-World Scenarios (Collections 1-4)
by: Jager, Gavin, et al.
Published: (2025)
by: Jager, Gavin, et al.
Published: (2025)
Just Shift It: Test-Time Prototype Shifting for Zero-Shot Generalization with Vision-Language Models
by: Sui, Elaine, et al.
Published: (2024)
by: Sui, Elaine, et al.
Published: (2024)
ECOR: Explainable CLIP for Object Recognition
by: Rasekh, Ali, et al.
Published: (2024)
by: Rasekh, Ali, et al.
Published: (2024)
From Data to Insights: A Covariate Analysis of the IARPA BRIAR Dataset for Multimodal Biometric Recognition Algorithms at Altitude and Range
by: Bolme, David S., et al.
Published: (2024)
by: Bolme, David S., et al.
Published: (2024)
Selective Classification Under Distribution Shifts
by: Liang, Hengyue, et al.
Published: (2024)
by: Liang, Hengyue, et al.
Published: (2024)
ExDDV: A New Dataset for Explainable Deepfake Detection in Video
by: Hondru, Vlad, et al.
Published: (2025)
by: Hondru, Vlad, et al.
Published: (2025)
A Shift in Perspective on Causality in Domain Generalization
by: Machlanski, Damian, et al.
Published: (2025)
by: Machlanski, Damian, et al.
Published: (2025)
SIDE: Sparse Information Disentanglement for Explainable Artificial Intelligence
by: Dubovik, Viktar, et al.
Published: (2025)
by: Dubovik, Viktar, et al.
Published: (2025)
Enhancing Vision Transformer Explainability Using Artificial Astrocytes
by: Echevarrieta-Catalan, Nicolas, et al.
Published: (2025)
by: Echevarrieta-Catalan, Nicolas, et al.
Published: (2025)
Provenance Networks: End-to-End Exemplar-Based Explainability
by: Kayyam, Ali, et al.
Published: (2025)
by: Kayyam, Ali, et al.
Published: (2025)
Constructing Fair Latent Space for Intersection of Fairness and Explainability
by: Joo, Hyungjun, et al.
Published: (2024)
by: Joo, Hyungjun, et al.
Published: (2024)
Impact of Adversarial Attacks on Deep Learning Model Explainability
by: Nur, Gazi Nazia, et al.
Published: (2024)
by: Nur, Gazi Nazia, et al.
Published: (2024)
Privacy Meets Explainability: A Comprehensive Impact Benchmark
by: Saifullah, Saifullah, et al.
Published: (2022)
by: Saifullah, Saifullah, et al.
Published: (2022)
Leaf-Based Plant Disease Detection and Explainable AI
by: Sagar, Saurav, et al.
Published: (2023)
by: Sagar, Saurav, et al.
Published: (2023)
CRD: Collaborative Representation Distance for Practical Anomaly Detection
by: Han, Chao, et al.
Published: (2023)
by: Han, Chao, et al.
Published: (2023)
Diverse Prototypical Ensembles Improve Robustness to Subpopulation Shift
by: To, Minh Nguyen Nhat, et al.
Published: (2025)
by: To, Minh Nguyen Nhat, et al.
Published: (2025)
A Causal Framework for Mitigating Data Shifts in Healthcare
by: Butler, Kurt, et al.
Published: (2026)
by: Butler, Kurt, et al.
Published: (2026)
Similar Items
-
Reliable Modeling of Distribution Shifts via Displacement-Reshaped Optimal Transport
by: Naumann, Philip, et al.
Published: (2026) -
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces
by: Chormai, Pattarawat, et al.
Published: (2022) -
Stereographic Spherical Sliced Wasserstein Distances
by: Tran, Huy, et al.
Published: (2024) -
Dataset Distillation via the Wasserstein Metric
by: Liu, Haoyang, et al.
Published: (2023) -
Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation
by: Lv, Jiaming, et al.
Published: (2024)