4.3k citations · 28.6k across the 204 of their papers we have counts for
15 papers · 2 filters
Deep Domain Confusion: Maximizing for Domain Invariance
Eric Tzeng, Judy Hoffman, Ning Zhang +2
Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias on a standard benchmark. Fine-tuning dee…
Detector Discovery in the Wild: Joint Multiple Instance and Representation Learning
Judy Hoffman, Deepak Pathak, Trevor Darrell +1
We develop methods for detector learning which exploit joint training over both weak and strong labels and which transfer learned perceptual representations from strongly-labeled a…
Learning Compact Convolutional Neural Networks with Nested Dropout
Chelsea Finn, Lisa Anne Hendricks, Trevor Darrell
Recently, nested dropout was proposed as a method for ordering representation units in autoencoders by their information content, without diminishing reconstruction cost. However,…
Fully Convolutional Multi-Class Multiple Instance Learning
Deepak Pathak, Evan Shelhamer, Jonathan Long +1
Multiple instance learning (MIL) can reduce the need for costly annotation in tasks such as semantic segmentation by weakening the required degree of supervision. We propose a nove…
Do Convnets Learn Correspondence?
Jonathan Long, Ning Zhang, Trevor Darrell
Convolutional neural nets (convnets) trained from massive labeled datasets have substantially improved the state-of-the-art in image classification and object detection. However, v…
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
Jeff Donahue, Lisa Anne Hendricks, Marcus Rohrbach +4
Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or "temporally deep", are effe…