4.3k citations · 15.4k across the 23 of their papers we have counts for
4 papers · 2 filters
One-Shot Adaptation of Supervised Deep Convolutional Models
Judy Hoffman, Eric Tzeng, Jeff Donahue +3
Dataset bias remains a significant barrier towards solving real world computer vision tasks. Though deep convolutional networks have proven to be a competitive approach for image c…
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell +1
Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that ty…
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals +4
We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks c…
Towards Adapting ImageNet to Reality: Scalable Domain Adaptation with Implicit Low-rank Transformations
Erik Rodner, Judy Hoffman, Jeff Donahue +2
Images seen during test time are often not from the same distribution as images used for learning. This problem, known as domain shift, occurs when training classifiers from object…