1k citations · 1.2k across the 11 of their papers we have counts for
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Interactive Visual Feature Search
Devon Ulrich, Ruth Fong
Many visualization techniques have been created to explain the behavior of computer vision models, but they largely consist of static diagrams that convey limited information. Inte…
Improving Data-Efficient Fossil Segmentation via Model Editing
Indu Panigrahi, Ryan Manzuk, Adam Maloof +1
Most computer vision research focuses on datasets containing thousands of images of commonplace objects. However, many high-impact datasets, such as those in medicine and the geosc…
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction
Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky +2
Despite the proliferation of explainable AI (XAI) methods, little is understood about end-users' explainability needs and behaviors around XAI explanations. To address this gap and…
Overlooked factors in concept-based explanations: Dataset choice, concept learnability, and human capability
Vikram V. Ramaswamy, Sunnie S. Y. Kim, Ruth Fong +1
Concept-based interpretability methods aim to explain deep neural network model predictions using a predefined set of semantic concepts. These methods evaluate a trained model on a…
ELUDE: Generating interpretable explanations via a decomposition into labelled and unlabelled features
Vikram V. Ramaswamy, Sunnie S. Y. Kim, Nicole Meister +2
Deep learning models have achieved remarkable success in different areas of machine learning over the past decade; however, the size and complexity of these models make them diffic…
Gender Artifacts in Visual Datasets
Nicole Meister, Dora Zhao, Angelina Wang +3
Gender biases are known to exist within large-scale visual datasets and can be reflected or even amplified in downstream models. Many prior works have proposed methods for mitigati…