Evan Shelhamer

Research topics

• Advanced Neural Network Applications · 3 papers
• Domain Adaptation and Few-Shot Learning · 2 papers
• Multimodal Machine Learning Applications · 2 papers
• Generative Adversarial Networks and Image Synthesis · 1 paper
• Advanced Image and Video Retrieval Techniques · 1 paper

Frequent co-authors

• Jonathan Long · 3 papers
• Trevor J. Darrell · 3 papers
• Yangqing Jia · 1 paper
• Jeff Donahue · 1 paper
• Sergey Karayev · 1 paper
• Ross Girshick · 1 paper
• Sergio Guadarrama · 1 paper

Papers

1. Fully convolutional networks for semantic segmentation2015 · 37,731 citations

   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, exceed the state-of-the-art in semantic segmentation. Our key insight is to build “fully convolutional” networks that take input of…

2. Fully Convolutional Networks for Semantic SegmentationIEEE Transactions on Pattern Analysis and Machine Intelligence · 2016 · 11,270 citations

   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, improve on the previous best result in semantic segmentation. Our key insight is to build "fully convolutional" networks that…

3. Caffe2014 · 11,223 citations

   Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose…