71 citations · 186 across the 38 of their papers we have counts for
7 papers · 1 filter
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…
Identification of Attack-Specific Signatures in Adversarial Examples
Hossein Souri, Pirazh Khorramshahi, Chun Pong Lau +2
The adversarial attack literature contains a myriad of algorithms for crafting perturbations which yield pathological behavior in neural networks. In many cases, multiple algorithm…
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models
Liam Fowl, Jonas Geiping, Wojtek Czaja +2
Federated learning has quickly gained popularity with its promises of increased user privacy and efficiency. Previous works have shown that federated gradient updates contain infor…
Datasets for Studying Generalization from Easy to Hard Examples
Avi Schwarzschild, Eitan Borgnia, Arjun Gupta +5
We describe new datasets for studying generalization from easy to hard examples.
Stochastic Training is Not Necessary for Generalization
Jonas Geiping, Micah Goldblum, Phillip E. Pope +2
It is widely believed that the implicit regularization of SGD is fundamental to the impressive generalization behavior we observe in neural networks. In this work, we demonstrate t…
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…