175 citations · 220 across the 8 of their papers we have counts for
9 papers · 1 filter
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 +1
Concept-based explanations for convolutional neural networks (CNNs) aim to explain model behavior and outputs using a pre-defined set of semantic concepts (e.g., the model recogniz…
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…
HIVE: Evaluating the Human Interpretability of Visual Explanations
Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy +2
As AI technology is increasingly applied to high-impact, high-risk domains, there have been a number of new methods aimed at making AI models more human interpretable. Despite the…
Cleaning and Structuring the Label Space of the iMet Collection 2020
Vivien Nguyen, Sunnie S. Y. Kim
The iMet 2020 dataset is a valuable resource in the space of fine-grained art attribution recognition, but we believe it has yet to reach its true potential. We document the unique…
[Re] Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias
Sunnie S. Y. Kim, Sharon Zhang, Nicole Meister +1
Singh et al. (2020) point out the dangers of contextual bias in visual recognition datasets. They propose two methods, CAM-based and feature-split, that better recognize an object…
Information-Theoretic Segmentation by Inpainting Error Maximization
Pedro Savarese, Sunnie S. Y. Kim, Michael Maire +2
We study image segmentation from an information-theoretic perspective, proposing a novel adversarial method that performs unsupervised segmentation by partitioning images into maxi…
Fair Attribute Classification through Latent Space De-biasing
Vikram V. Ramaswamy, Sunnie S. Y. Kim, Olga Russakovsky
Fairness in visual recognition is becoming a prominent and critical topic of discussion as recognition systems are deployed at scale in the real world. Models trained from data in…
Deformable Style Transfer
Sunnie S. Y. Kim, Nicholas Kolkin, Jason Salavon +1
Both geometry and texture are fundamental aspects of visual style. Existing style transfer methods, however, primarily focus on texture, almost entirely ignoring geometry. We propo…