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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...
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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 ...
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11/28/2022
Interactive Visual Feature Search
Many visualization techniques have been created to help explain the beha...
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10/08/2022
Improving Fine-Grain Segmentation via Interpretable Modifications: A Case Study in Fossil Segmentation
Most interpretability research focuses on datasets containing thousands ...
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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...
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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...
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06/18/2022
Gender Artifacts in Visual Datasets
Gender biases are known to exist within large-scale visual datasets and ...
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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...
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12/06/2021
HIVE: Evaluating the Human Interpretability of Visual Explanations
As machine learning is increasingly applied to high-impact, high-risk do...
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11/15/2020
Debiasing Convolutional Neural Networks via Meta Orthogonalization
While deep learning models often achieve strong task performance, their ...
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09/18/2020
Contextual Semantic Interpretability
Convolutional neural networks (CNN) are known to learn an image represen...
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04/15/2020
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
With the recent wave of progress in artificial intelligence (AI) has com...
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04/06/2020
There and Back Again: Revisiting Backpropagation Saliency Methods
Saliency methods seek to explain the predictions of a model by producing...
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03/09/2020
Multi-modal Self-Supervision from Generalized Data Transformations
Self-supervised learning has advanced rapidly, with several results beat...
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10/23/2019
Occlusions for Effective Data Augmentation in Image Classification
Deep networks for visual recognition are known to leverage "easy to reco...
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10/19/2019
NormGrad: Finding the Pixels that Matter for Training
The different families of saliency methods, either based on contrastive ...
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10/18/2019
Understanding Deep Networks via Extremal Perturbations and Smooth Masks
The problem of attribution is concerned with identifying the parts of an...
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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...
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04/11/2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation
As machine learning algorithms are increasingly applied to high impact y...
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03/16/2017
