Ruth Fong

According to our database, Ruth Fong authored at least 28 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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

Gender Artifacts in Visual Datasets.

Nicole Meister, Dora Zhao, Angelina Wang, Vikram V. Ramaswamy, Ruth Fong, Olga Russakovsky

Proceedings of the IEEE/CVF International Conference on Computer Vision, 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

Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 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

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Improving Data-Efficient Fossil Segmentation via Model Editing.

Indu Panigrahi, Ryan Manzuk, Adam Maloof, Ruth Fong

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

"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

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 2023

2022

Interactive Visual Feature Search.

Devon Ulrich, Ruth Fong

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

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

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

HIVE: Evaluating the Human Interpretability of Visual Explanations.

Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy, Ruth Fong, Olga Russakovsky

Proceedings of the Computer Vision - ECCV 2022, 2022

2021

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

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020

Understanding convolutional neural networks.

Ruth Fong

PhD thesis, 2020

Debiasing Convolutional Neural Networks via Meta Orthogonalization.

Kurtis Evan David, Qiang Liu, Ruth Fong

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, Sara Hooker, Jade Leung, Andrew Trask, Emma Bluemke, Jonathan Lebensold, Cullen O'Keefe, Mark Koren, Théo Ryffel, J. B. Rubinovitz, Tamay Besiroglu, Federica Carugati, Jack Clark, Peter Eckersley, Sarah de Haas, Maritza Johnson, Ben Laurie, Alex Ingerman, Igor Krawczuk, Amanda Askell, Rosario Cammarota, Andrew Lohn, David Krueger, Charlotte Stix, Peter Henderson, Logan Graham, Carina Prunkl, Bianca Martin, Elizabeth Seger, Noa Zilberman, Seán Ó hÉigeartaigh, Frens Kroeger, Girish Sastry, Rebecca Kagan, Adrian Weller, Brian Tse, Elizabeth Barnes, Allan Dafoe, Paul Scharre, Ariel Herbert-Voss, Martijn Rasser, Shagun Sodhani, Carrick Flynn, Thomas Krendl Gilbert, Lisa Dyer, Saif Khan, Yoshua Bengio, Markus Anderljung

CoRR, 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

Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning.

Iro Laina, Ruth Fong, Andrea Vedaldi

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

xxAI - Beyond Explainable Artificial Intelligence.

Andreas Holzinger, Randy Goebel, Ruth Fong, Taesup Moon, Klaus-Robert Müller, Wojciech Samek

Proceedings of the xxAI - Beyond Explainable AI, 2020

There and Back Again: Revisiting Backpropagation Saliency Methods.

Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji, Andrea Vedaldi

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Contextual Semantic Interpretability.

Diego Marcos, Ruth Fong, Sylvain Lobry, Rémi Flamary, Nicolas Courty, Devis Tuia

Proceedings of the Computer Vision - ACCV 2020 - 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30, 2020

2019

Explanations for Attributing Deep Neural Network Predictions.

Ruth Fong, Andrea Vedaldi

Proceedings of the Explainable AI: Interpreting, 2019

Occlusions for Effective Data Augmentation in Image Classification.

Ruth Fong, Andrea Vedaldi

CoRR, 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

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshops, 2019

Understanding Deep Networks via Extremal Perturbations and Smooth Masks.

Ruth Fong, Mandela Patrick, Andrea Vedaldi

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

2018

Net2Vec: Quantifying and Explaining How Concepts Are Encoded by Filters in Deep Neural Networks.

Ruth Fong, Andrea Vedaldi

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017

Using Human Brain Activity to Guide Machine Learning.

Ruth Fong, Walter J. Scheirer, David D. Cox

CoRR, 2017

Interpretable Explanations of Black Boxes by Meaningful Perturbation.

Ruth C. Fong, Andrea Vedaldi

Proceedings of the IEEE International Conference on Computer Vision, 2017