71 citations · 186 across the 38 of their papers we have counts for
7 papers · 1 filter
Preserving Cross-Modal Consistency for CLIP-based Class-Incremental Learning
Haoran Chen, Houze Xu, Micah Goldblum +2
Class-incremental learning (CIL) enables models to continuously learn new categories from sequential tasks without forgetting previously acquired knowledge. While recent advances i…
Adaptive Retention & Correction: Test-Time Training for Continual Learning
Haoran Chen, Micah Goldblum, Zuxuan Wu +1
Continual learning, also known as lifelong learning or incremental learning, refers to the process by which a model learns from a stream of incoming data over time. A common proble…
Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition
Samuel Dooley, Rhea Sanjay Sukthanker, John P. Dickerson +3
Face recognition systems are widely deployed in safety-critical applications, including law enforcement, yet they exhibit bias across a range of socio-demographic dimensions, such…
Active Learning at the ImageNet Scale
Zeyad Ali Sami Emam, Hong-Min Chu, Ping-Yeh Chiang +4
Active learning (AL) algorithms aim to identify an optimal subset of data for annotation, such that deep neural networks (DNN) can achieve better performance when trained on this l…
Comparing Human and Machine Bias in Face Recognition
Samuel Dooley, Ryan Downing, George Wei +10
Much recent research has uncovered and discussed serious concerns of bias in facial analysis technologies, finding performance disparities between groups of people based on perceiv…
Towards Transferable Adversarial Attacks on Vision Transformers
Zhipeng Wei, Jingjing Chen, Micah Goldblum +3
Vision transformers (ViTs) have demonstrated impressive performance on a series of computer vision tasks, yet they still suffer from adversarial examples. % crafted in a similar fa…
Encoding Robustness to Image Style via Adversarial Feature Perturbations
Manli Shu, Zuxuan Wu, Micah Goldblum +1
Adversarial training is the industry standard for producing models that are robust to small adversarial perturbations. However, machine learning practitioners need models that are…