4.3k citations · 12k across the 31 of their papers we have counts for
12 papers · 1 filter
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…
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…
Fully Convolutional Networks for Semantic Segmentation
Jonathan Long, Evan Shelhamer, Trevor Darrell
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, excee…
DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks
Tao Chen, Damian Borth, Trevor Darrell +1
This paper introduces a visual sentiment concept classification method based on deep convolutional neural networks (CNNs). The visual sentiment concepts are adjective noun pairs (A…