4.3k citations · 15.4k across the 23 of their papers we have counts for
5 papers · 1 filter
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…
Efficient Learning of Domain-invariant Image Representations
Judy Hoffman, Erik Rodner, Jeff Donahue +2
We present an algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers. Specifically, we fo…