Ruth Fong | DeepAI

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∙ 05/15/2023

Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application

Trust is an important factor in people's interactions with AI systems. H...

0 Sunnie S. Y. Kim, et al. ∙

∙ 03/27/2023

UFO: A unified method for controlling Understandability and Faithfulness Objectives in concept-based explanations for CNNs

Concept-based explanations for convolutional neural networks (CNNs) aim ...

0 Vikram V. Ramaswamy, et al. ∙

∙ 11/28/2022

Interactive Visual Feature Search

Many visualization techniques have been created to help explain the beha...

0 Devon Ulrich, et al. ∙

∙ 10/08/2022

Improving Fine-Grain Segmentation via Interpretable Modifications: A Case Study in Fossil Segmentation

Most interpretability research focuses on datasets containing thousands ...

0 Indu Panigrahi, et al. ∙

∙ 10/02/2022

"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction

Despite the proliferation of explainable AI (XAI) methods, little is und...

11 Sunnie S. Y. Kim, et al. ∙

∙ 07/20/2022

Overlooked factors in concept-based explanations: Dataset choice, concept salience, and human capability

Concept-based interpretability methods aim to explain deep neural networ...

43 Vikram V. Ramaswamy, et al. ∙

∙ 06/18/2022

Gender Artifacts in Visual Datasets

Gender biases are known to exist within large-scale visual datasets and ...

0 Nicole Meister, et al. ∙

∙ 06/15/2022

ELUDE: Generating interpretable explanations via a decomposition into labelled and unlabelled features

Deep learning models have achieved remarkable success in different areas...

27 Vikram V. Ramaswamy, et al. ∙

∙ 12/06/2021

HIVE: Evaluating the Human Interpretability of Visual Explanations

As machine learning is increasingly applied to high-impact, high-risk do...

6 Sunnie S. Y. Kim, et al. ∙

∙ 11/15/2020

Debiasing Convolutional Neural Networks via Meta Orthogonalization

While deep learning models often achieve strong task performance, their ...

3 Kurtis Evan David, et al. ∙

∙ 09/18/2020

Contextual Semantic Interpretability

Convolutional neural networks (CNN) are known to learn an image represen...

12 Diego Marcos, et al. ∙

∙ 04/15/2020

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

With the recent wave of progress in artificial intelligence (AI) has com...

0 Miles Brundage, et al. ∙

∙ 04/06/2020

There and Back Again: Revisiting Backpropagation Saliency Methods

Saliency methods seek to explain the predictions of a model by producing...

18 Sylvestre-Alvise Rebuffi, et al. ∙

∙ 03/09/2020

Multi-modal Self-Supervision from Generalized Data Transformations

Self-supervised learning has advanced rapidly, with several results beat...

12 Mandela Patrick, et al. ∙

∙ 10/23/2019

Occlusions for Effective Data Augmentation in Image Classification

Deep networks for visual recognition are known to leverage "easy to reco...

18 Ruth Fong, et al. ∙

∙ 10/19/2019

NormGrad: Finding the Pixels that Matter for Training

The different families of saliency methods, either based on contrastive ...

0 Sylvestre-Alvise Rebuffi, et al. ∙

∙ 10/18/2019

Understanding Deep Networks via Extremal Perturbations and Smooth Masks

The problem of attribution is concerned with identifying the parts of an...

17 Ruth Fong, et al. ∙

∙ 01/10/2018

Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks

In an effort to understand the meaning of the intermediate representatio...

0 Ruth Fong, et al. ∙

∙ 04/11/2017

Interpretable Explanations of Black Boxes by Meaningful Perturbation

As machine learning algorithms are increasingly applied to high impact y...

0 Ruth Fong, et al. ∙

∙ 03/16/2017

Using Human Brain Activity to Guide Machine Learning

Machine learning is a field of computer science that builds algorithms t...

0 Ruth Fong, et al. ∙

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