Interpretable Explanations of Black Boxes by Meaningful Perturbation. - Ruth C. Fong; Andrea Vedaldi (ICCV, 2017)

Using Human Brain Activity to Guide Machine Learning. - Ruth Fong; Walter J. Scheirer; David D. Cox (CoRR, 2017)

Net2Vec: Quantifying and Explaining How Concepts Are Encoded by Filters in Deep Neural Networks. - Ruth Fong; Andrea Vedaldi (CVPR, 2018)

Understanding Deep Networks via Extremal Perturbations and Smooth Masks. - Ruth Fong; Mandela Patrick; Andrea Vedaldi (ICCV, 2019)

Occlusions for Effective Data Augmentation in Image Classification. - Ruth Fong (ICCV Workshops, 2019)

NormGrad: Finding the Pixels that Matter for Training. - Sylvestre-Alvise Rebuffi; Ruth Fong; Xu Ji; Hakan Bilen; Andrea Vedaldi (CoRR, 2019)

Occlusions for Effective Data Augmentation in Image Classification. - Ruth Fong; Andrea Vedaldi (CoRR, 2019)

Explanations for Attributing Deep Neural Network Predictions. - Ruth Fong; Andrea Vedaldi (Explainable AI, 2019)

Contextual Semantic Interpretability. - Diego Marcos; Ruth Fong; Sylvain Lobry; Rémi Flamary; Nicolas Courty; Devis Tuia (ACCV (4), 2020)

There and Back Again: Revisiting Backpropagation Saliency Methods. - Sylvestre-Alvise Rebuffi; Ruth Fong; Xu Ji; Andrea Vedaldi (CVPR, 2020)

xxAI - Beyond Explainable Artificial Intelligence. - Andreas Holzinger; Randy Goebel; Ruth Fong; Taesup Moon; Klaus-Robert Müller; Wojciech Samek (xxAI@ICML, 2020)

Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning. - Iro Laina; Ruth Fong; Andrea Vedaldi (NeurIPS, 2020)

Multi-modal Self-Supervision from Generalized Data Transformations. - Mandela Patrick; Yuki Markus Asano; Ruth Fong; João F. Henriques; Geoffrey Zweig; Andrea Vedaldi (CoRR, 2020)

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims. - Miles Brundage; Shahar Avin; Jasmine Wang; Haydn Belfield; Gretchen Krueger; Gillian K. Hadfield; Heidy Khlaaf; Jingying Yang; Helen Toner; Ruth Fong; Tegan Maharaj; Pang Wei Koh; et al. (CoRR, 2020)

Debiasing Convolutional Neural Networks via Meta Orthogonalization. - Kurtis Evan David; Qiang Liu; Ruth Fong (CoRR, 2020)

Understanding convolutional neural networks. - Ruth Fong (2020)

On Compositions of Transformations in Contrastive Self-Supervised Learning. - Mandela Patrick; Yuki Markus Asano; Polina Kuznetsova; Ruth Fong; João F. Henriques; Geoffrey Zweig; Andrea Vedaldi (ICCV, 2021)

HIVE: Evaluating the Human Interpretability of Visual Explanations. - Sunnie S. Y. Kim; Nicole Meister; Vikram V. Ramaswamy; Ruth Fong; Olga Russakovsky (ECCV (12), 2022)

ELUDE: Generating interpretable explanations via a decomposition into labelled and unlabelled features. - Vikram V. Ramaswamy; Sunnie S. Y. Kim; Nicole Meister; Ruth Fong; Olga Russakovsky (CoRR, 2022)

Overlooked factors in concept-based explanations: Dataset choice, concept salience, and human capability. - Vikram V. Ramaswamy; Sunnie S. Y. Kim; Ruth Fong; Olga Russakovsky (CoRR, 2022)

Improving Fine-Grain Segmentation via Interpretable Modifications: A Case Study in Fossil Segmentation. - Indu Panigrahi; Ryan Manzuk; Adam Maloof; Ruth Fong (CoRR, 2022)

"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction. - Sunnie S. Y. Kim; Elizabeth Anne Watkins; Olga Russakovsky; Ruth Fong; Andrés Monroy-Hernández (CHI, 2023)

Improving Data-Efficient Fossil Segmentation via Model Editing. - Indu Panigrahi; Ryan Manzuk; Adam Maloof; Ruth Fong (CVPR Workshops, 2023)

Overlooked Factors in Concept-Based Explanations: Dataset Choice, Concept Learnability, and Human Capability. - Vikram V. Ramaswamy; Sunnie S. Y. Kim; Ruth Fong; Olga Russakovsky (CVPR, 2023)

Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application. - Sunnie S. Y. Kim; Elizabeth Anne Watkins; Olga Russakovsky; Ruth Fong; Andrés Monroy-Hernández (FAccT, 2023)

Gender Artifacts in Visual Datasets. - Nicole Meister; Dora Zhao; Angelina Wang; Vikram V. Ramaswamy; Ruth Fong; Olga Russakovsky (ICCV, 2023)

UFO: A unified method for controlling Understandability and Faithfulness Objectives in concept-based explanations for CNNs. - Vikram V. Ramaswamy; Sunnie S. Y. Kim; Ruth Fong; Olga Russakovsky (CoRR, 2023)

Interactive Visual Feature Search. - Devon Ulrich; Ruth Fong (XAI4CV, 2024)

Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations. - Indu Panigrahi; Sunnie S. Y. Kim; Amna Liaqat; Rohan Jinturkar; Olga Russakovsky; Ruth Fong; Parastoo Abtahi (CHI Extended Abstracts, 2025)