50 citations · 84 across the 10 of their papers we have counts for
14 papers
Evaluating Intellectual Property Guardrails of Generative Image Models: A Technical Report
Austin T. Hoag, Apostolos Modas, Yunhao Ba +9
Generative image models are capable of producing images that bear a strong resemblance to, or replicate, recognizable intellectual property (IP). In this technical report, we prese…
Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes
Yusuke Hirota, Jerone T. A. Andrews, Dora Zhao +4
We tackle societal bias in image-text datasets by removing spurious correlations between protected groups and image attributes. Traditional methods only target labeled attributes,…
Ethical Considerations for Responsible Data Curation
Jerone T. A. Andrews, Dora Zhao, William Thong +3
Human-centric computer vision (HCCV) data curation practices often neglect privacy and bias concerns, leading to dataset retractions and unfair models. HCCV datasets constructed th…
Robustness and invariance properties of image classifiers
Apostolos Modas
Deep neural networks have achieved impressive results in many image classification tasks. However, since their performance is usually measured in controlled settings, it is importa…
Data augmentation with mixtures of max-entropy transformations for filling-level classification
Apostolos Modas, Andrea Cavallaro, Pascal Frossard
We address the problem of distribution shifts in test-time data with a principled data augmentation scheme for the task of content-level classification. In such a task, properties…
PRIME: A few primitives can boost robustness to common corruptions
Apostolos Modas, Rahul Rade, Guillermo Ortiz-Jiménez +2
Despite their impressive performance on image classification tasks, deep networks have a hard time generalizing to unforeseen corruptions of their data. To fix this vulnerability,…