arXiv Machine Learning
Jun 3
Attribution via Distributional Paths for Information Revelation
arXiv:2606. 03885v1 Announce Type: new Abstract: Feature attribution methods explain predictions by assigning importance scores to input features.
On Pitfalls of $\textit{RemOve-And-Retrain}$: Data Processing Inequality Perspective
arXiv:2304. 13836v5 Announce Type: replace-cross Abstract: The RemOve-And-Retrain (ROAR) benchmark is widely used to evaluate feature attribution methods, yet its validity remains underexplored from an information-theoretic perspective.
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arXiv Machine Learning
Jun 10
XtrAIn: Training-Guided Occlusion for Feature Attribution
arXiv:2606. 10877v1 Announce Type: new Abstract: Occlusion-based attribution methods provide an intuitive way to estimate feature importance by perturbing input features and measuring the resulting change in model output.
arXiv Machine Learning
Jun 16
Enhancing Visual Feature Attribution via Weighted Integrated Gradients
arXiv:2505. 03201v4 Announce Type: replace-cross Abstract: Integrated Gradients (IG) is a widely used attribution method in explainable AI, particularly in computer vision applications where reliable feature attribution is essential.
arXiv AI
Jul 23
In-Run Data Shapley for Adam Optimizer
arXiv:2602. 00329v4 Announce Type: replace-cross Abstract: Reliable data attribution is essential for mitigating bias and reducing computational waste in modern machine learning, with the Shapley value serving as the theoretical gold standard.
Hugging Face Trending Papers
Jul 30
Scaling Vision-Language Models Is Not Enough to Mitigate Bias
Vision-Language Models (VLMs) such as CLIP are now foundational to multimodal systems, yet their robustness to spurious correlations remains poorly understood at scale. We present the first large-scale empirical study of 194 publicly available VLMs, including 16 model families, covering a wide range of model sizes, 24 training datasets, and three evaluation benchmarks, namely ImageNet (overall performance), CelebA (typical single-attribute bias), and UrbanCars (complex multi-attribute biases).
arXiv AI
Jun 10
Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classification
arXiv:2411. 05698v3 Announce Type: replace-cross Abstract: Convolutional Neural Networks (CNNs) have shown remarkable performance in image classification.
arXiv Machine Learning
Jun 2
SurrogateSHAP: Training-Free Contributor Attribution for Text-to-Image (T2I) Models
arXiv:2601. 22276v2 Announce Type: replace Abstract: As Text-to-Image (T2I) diffusion models are increasingly used in real-world creative workflows, a principled framework for valuing contributors who provide a collection of data is essential for fair compensation and sustainable data marketplaces.
arXiv AI
Jun 2
GUDA: Counterfactual Group-wise Training Data Attribution for Diffusion Models via Unlearning
arXiv:2601. 22651v2 Announce Type: replace-cross Abstract: Training-data attribution for vision generative models aims to identify which training data influenced a given output.
arXiv AI
Jun 16
Mitigating Object Hallucinations in LVLMs via Attention Imbalance Rectification
arXiv:2603. 24058v2 Announce Type: replace-cross Abstract: Object hallucination in Large Vision-Language Models (LVLMs) severely compromises their reliability in real-world applications, posing a critical barrier to their deployment in high-stakes scenarios such as autonomous driving and medical image analysis.
arXiv AI
Jul 29
Are the High-weight Neurons the Important Ones in Image Classification Neural Networks?
arXiv:2607. 25529v1 Announce Type: new Abstract: As neural network models for image classification advance, neurons play critical roles in pruning, backdoor defense, and interpretability.
arXiv AI
Jun 12
On the Reliability of Cue Conflict and Beyond
arXiv:2603. 10834v3 Announce Type: replace-cross Abstract: Understanding how neural networks rely on visual cues offers a human-interpretable view of their internal decision processes.
arXiv AI
Aug 5
Measuring Explainer Stability via Attribution Separability
arXiv:2608. 02697v1 Announce Type: cross Abstract: Attribution methods (AMs) assign an importance score to each feature and are widely adopted to explain black-box models.