26 citations · 91 across the 22 of their papers we have counts for
4 papers · 1 filter
On-target Adaptation
Dequan Wang, Shaoteng Liu, Sayna Ebrahimi +2
Domain adaptation seeks to mitigate the shift between training on the \emph{source} domain and testing on the \emph{target} domain. Most adaptation methods rely on the source data…
Predicting with Confidence on Unseen Distributions
Devin Guillory, Vaishaal Shankar, Sayna Ebrahimi +2
Recent work has shown that the performance of machine learning models can vary substantially when models are evaluated on data drawn from a distribution that is close to but differ…
Region-level Active Detector Learning
Michael Laielli, Giscard Biamby, Dian Chen +6
Active learning for object detection is conventionally achieved by applying techniques developed for classification in a way that aggregates individual detections into image-level…
Self-Supervised Pretraining Improves Self-Supervised Pretraining
Colorado J. Reed, Xiangyu Yue, Ani Nrusimha +9
While self-supervised pretraining has proven beneficial for many computer vision tasks, it requires expensive and lengthy computation, large amounts of data, and is sensitive to da…