Cover Image

Robustness of Visual Explanations to Common Data Augmentation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tětková, Lenka, Hansen, Lars Kai
Format:	Preprint
Published:	2023
Subjects:	Computer Vision and Pattern Recognition Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Challenges in explaining deep learning models for data with biological variation
by: Tětková, Lenka, et al.
Published: (2024)

From Colors to Classes: Emergence of Concepts in Vision Transformers
by: Dorszewski, Teresa, et al.
Published: (2025)

A Recipe for Unbounded Data Augmentation in Visual Reinforcement Learning
by: Almuzairee, Abdulaziz, et al.
Published: (2024)

Large Vision Models Can Solve Mental Rotation Problems
by: Mason, Sebastian Ray, et al.
Published: (2025)

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation
by: Hu, Jiaming, et al.
Published: (2025)

On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning
by: Satou, Hana, et al.
Published: (2025)

Discovering and Mitigating Visual Biases through Keyword Explanation
by: Kim, Younghyun, et al.
Published: (2023)

Architecture-Aware Explanation Auditing for Industrial Visual Inspection
by: Jia, Sibo, et al.
Published: (2026)

Contrastive Visual Data Augmentation
by: Zhou, Yu, et al.
Published: (2025)

Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape
by: Chen, Tiejin, et al.
Published: (2024)

DocVXQA: Context-Aware Visual Explanations for Document Question Answering
by: Souibgui, Mohamed Ali, et al.
Published: (2025)

Towards Self-Explainable Document Visual Question Answering with Chain-of-Explanation Predictions
by: Indrehus, Kjetil, et al.
Published: (2026)

Data-centric Prediction Explanation via Kernelized Stein Discrepancy
by: Sarvmaili, Mahtab, et al.
Published: (2024)

Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness
by: Srinivas, Suraj, et al.
Published: (2023)

Out-of-Domain Robustness via Targeted Augmentations
by: Gao, Irena, et al.
Published: (2023)

Improving Visual Grounding by Encouraging Consistent Gradient-based Explanations
by: Yang, Ziyan, et al.
Published: (2022)

On-the-Fly Data Augmentation via Gradient-Guided and Sample-Aware Influence Estimation
by: Yang, Suorong, et al.
Published: (2025)

Data Augmentation For Small Object using Fast AutoAugment
by: Yoon, DaeEun, et al.
Published: (2025)

Generative Spatiotemporal Data Augmentation
by: Zhou, Jinfan, et al.
Published: (2025)

Stylized Synthetic Augmentation further improves Corruption Robustness
by: Siedel, Georg, et al.
Published: (2025)

Approximate Nullspace Augmented Finetuning for Robust Vision Transformers
by: Liu, Haoyang, et al.
Published: (2024)

MI CAM: Mutual Information Weighted Activation Mapping for Causal Visual Explanations of Convolutional Neural Networks
by: Iyer, Ram S, et al.
Published: (2025)

ZACH-ViT: A Zero-Token Vision Transformer with ShuffleStrides Data Augmentation for Robust Lung Ultrasound Classification
by: Angelakis, Athanasios, et al.
Published: (2025)

Right Predictions, Misleading Explanations: On the Vulnerability of Vision-Language Model Explanations
by: Babadi, Narges, et al.
Published: (2026)

Model Debiasing by Learnable Data Augmentation
by: Morerio, Pietro, et al.
Published: (2024)

Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations
by: Qu, Helen, et al.
Published: (2024)

Compositional Adversarial Training for Robust Visual Watermarking
by: Satheesh, Anirudh, et al.
Published: (2026)

MADI: Masking-Augmented Diffusion with Inference-Time Scaling for Visual Editing
by: Kadambi, Shreya, et al.
Published: (2025)

DiffAug: A Diffuse-and-Denoise Augmentation for Training Robust Classifiers
by: Sastry, Chandramouli, et al.
Published: (2023)

Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces
by: Wang, Ruoqi, et al.
Published: (2025)

AugLoss: A Robust Augmentation-based Fine Tuning Methodology
by: Otstot, Kyle, et al.
Published: (2022)

Data-Augmented Quantization-Aware Knowledge Distillation
by: Kur, Justin, et al.
Published: (2025)

Controlled Face Manipulation and Synthesis for Data Augmentation
by: Kirchner, Joris, et al.
Published: (2026)

Optimal Layer Selection for Latent Data Augmentation
by: Takase, Tomoumi, et al.
Published: (2024)

Activation Matching for Explanation Generation
by: Suhail, Pirzada, et al.
Published: (2025)

Linear Explanations for Individual Neurons
by: Oikarinen, Tuomas, et al.
Published: (2024)

Sensitivity as a Double-Edged Sword: A Trade-off Between Discriminability and Adversarial Robustness
by: Wang, Kai
Published: (2026)

Unsupervised Interpretable Basis Extraction for Concept-Based Visual Explanations
by: Doumanoglou, Alexandros, et al.
Published: (2023)

Random Window Augmentations for Deep Learning Robustness in CT and Liver Tumor Segmentation
by: Østmo, Eirik A., et al.
Published: (2025)

Understanding the Detrimental Class-level Effects of Data Augmentation
by: Kirichenko, Polina, et al.
Published: (2023)