arXiv:2403. 06013v2 Announce Type: replace Abstract: This paper delves into the critical area of deep learning robustness, challenging the conventional belief that classification robustness and explanation robustness in image classification systems are inherently correlated.
arXiv:2607. 29614v1 Announce Type: cross Abstract: The rapid adoption of deep learning models in high-risk domains has intensified the need for trustworthy Explainable Artificial Intelligence (XAI).
The paper introduces eXplaining to Learn (eX2L), an interpretable framework that regularizes a classifier by penalizing similarity between Grad‑CAM maps of the main label classifier and a confounder classifier. This approach decorrelates confounding features from latent representations during training. On the Spawrious Many‑to‑Many Hard Challenge benchmark, eX2L outperforms the current state‑of‑the‑art by 5.49% in average accuracy and 10.90% in worst‑group accuracy, while also demonstrating functional domain invariance through explicit label‑nuisance decoupling.
arXiv:2607. 06637v1 Announce Type: new Abstract: In this work, we propose a unified approach for diagnosing misclassification and assessing the robustness of black-box classifiers.
arXiv:2411. 05698v3 Announce Type: replace-cross Abstract: Convolutional Neural Networks (CNNs) have shown remarkable performance in image classification.
ICON Decomposition is a new method for explaining deep neural networks by quantifying how much variance each concept explains in a network layer after accounting for all other concepts and the outcome. Unlike previous concept‑based methods that evaluate concepts in isolation, ICON can distinguish genuine model reliance from spurious correlations. Experiments on synthetic data, skin‑lesion, and brain‑imaging models show that ICON recovers concept importance more accurately, isolates truly relied‑upon concepts, and provides sparse explanations validated through retraining and out‑of‑distribution testing.
arXiv:2608. 04442v1 Announce Type: new Abstract: Robustness to natural corruptions remains a fundamental challenge for deep neural networks.
Explainable Deepfake Detection with Feature-robust Augmentation and Evidence-grounded Explanation Optimization proposes a new framework that improves deepfake detection and interpretability. The approach introduces Feature-robust Augmentation—diversified degradation-aware strategies combined with supervised contrastive learning and a mean-teacher architecture—to maintain accuracy on low-quality images. For explanations, it employs evidence-grounded preference optimization, guiding the model to focus on genuine manipulation traces by learning from chosen-rejected explanation pairs that omit evidence or inject irrelevant details. The method achieved first place in the ACM Multimedia 2026 Explainable Deepfake Detection Challenge and is publicly available on GitHub.
arXiv:2608. 15731v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) deployed in high-risk domains, such as healthcare and autonomous driving, must be not only accurate but also understandable to ensure user trust.
The study investigates whether pathology foundation models (PFMs) carry center-related biases into whole-slide image (WSI) classification. By training models with increasing class-center correlations and evaluating six PFMs across four datasets and two MIL aggregators, the authors introduce the Area Under the Cramér's V Curve (AUCC) to measure both accuracy and degradation due to spurious correlations. Results reveal that center information propagates to WSI predictions, with robustness varying by PFM and MIL strategy, and that ComBat harmonization does not consistently improve robustness.
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:2606. 07180v1 Announce Type: cross Abstract: The growing demand for transparency in automated decision-making has propelled eXplainable Artificial Intelligence (XAI) to the forefront of machine learning research.
arXiv:2608.26083v2 Announce Type: replace-cross Abstract: Deep neural networks often exploit spurious associations, a failure known as shortcut learning. Auditing for shortcuts requires testing many...