Evan Shelhamer

According to our database, Evan Shelhamer authored at least 33 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Adaptive Randomized Smoothing: Certifying Multi-Step Defences against Adversarial Examples.

Saiyue Lyu, Shadab Shaikh, Frederick Shpilevskiy, Evan Shelhamer, Mathias Lécuyer

CoRR, 2024

2023

Exploring Simple and Transferable Recognition-Aware Image Processing.

Zhuang Liu, Hung-Ju Wang, Tinghui Zhou, Zhiqiang Shen, Bingyi Kang, Evan Shelhamer, Trevor Darrell

IEEE Trans. Pattern Anal. Mach. Intell., March, 2023

Back to the Source: Diffusion-Driven Adaptation to Test-Time Corruption.

Jin Gao, Jialing Zhang, Xihui Liu, Trevor Darrell, Evan Shelhamer, Dequan Wang

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts.

Francesco Croce, Sylvestre-Alvise Rebuffi, Evan Shelhamer, Sven Gowal

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Where Should I Spend My FLOPS? Efficiency Evaluations of Visual Pre-training Methods.

Skanda Koppula, Yazhe Li, Evan Shelhamer, Andrew Jaegle, Nikhil Parthasarathy, Relja Arandjelovic, João Carreira, Olivier J. Hénaff

CoRR, 2022

Back to the Source: Diffusion-Driven Test-Time Adaptation.

Jin Gao, Jialing Zhang, Xihui Liu, Trevor Darrell, Evan Shelhamer, Dequan Wang

CoRR, 2022

Hierarchical Perceiver.

João Carreira, Skanda Koppula, Daniel Zoran, Adrià Recasens, Catalin Ionescu, Olivier J. Hénaff, Evan Shelhamer, Relja Arandjelovic, Matthew M. Botvinick, Oriol Vinyals, Karen Simonyan, Andrew Zisserman, Andrew Jaegle

CoRR, 2022

Evaluating the Adversarial Robustness of Adaptive Test-time Defenses.

Francesco Croce, Sven Gowal, Thomas Brunner, Evan Shelhamer, Matthias Hein, A. Taylan Cemgil

Proceedings of the International Conference on Machine Learning, 2022

Perceiver IO: A General Architecture for Structured Inputs & Outputs.

Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier J. Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira

Proceedings of the Tenth International Conference on Learning Representations, 2022

Anytime Dense Prediction with Confidence Adaptivity.

Zhuang Liu, Zhiqiu Xu, Hung-Ju Wang, Trevor Darrell, Evan Shelhamer

Proceedings of the Tenth International Conference on Learning Representations, 2022

Object Discovery and Representation Networks.

Olivier J. Hénaff, Skanda Koppula, Evan Shelhamer, Daniel Zoran, Andrew Jaegle, Andrew Zisserman, João Carreira, Relja Arandjelovic

Proceedings of the Computer Vision - ECCV 2022, 2022

2021

On-target Adaptation.

Dequan Wang, Shaoteng Liu, Sayna Ebrahimi, Evan Shelhamer, Trevor Darrell

CoRR, 2021

Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks.

Dequan Wang, An Ju, Evan Shelhamer, David A. Wagner, Trevor Darrell

CoRR, 2021

Confidence Adaptive Anytime Pixel-Level Recognition.

Zhuang Liu, Trevor Darrell, Evan Shelhamer

CoRR, 2021

Tent: Fully Test-Time Adaptation by Entropy Minimization.

Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno A. Olshausen, Trevor Darrell

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

It Is Likely That Your Loss Should be a Likelihood.

Mark Hamilton, Evan Shelhamer, William T. Freeman

CoRR, 2020

Fully Test-time Adaptation by Entropy Minimization.

Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno A. Olshausen, Trevor Darrell

CoRR, 2020

2019

Dynamic Scale Inference by Entropy Minimization.

Dequan Wang, Evan Shelhamer, Bruno A. Olshausen, Trevor Darrell

CoRR, 2019

Blurring the Line Between Structure and Learning to Optimize and Adapt Receptive Fields.

Evan Shelhamer, Dequan Wang, Trevor Darrell

CoRR, 2019

Infinite Mixture Prototypes for Few-shot Learning.

Kelsey R. Allen, Evan Shelhamer, Hanul Shin, Joshua B. Tenenbaum

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Few-Shot Segmentation Propagation with Guided Networks.

Kate Rakelly, Evan Shelhamer, Trevor Darrell, Alexei A. Efros, Sergey Levine

CoRR, 2018

Learning Rich Image Representation with Deep Layer Aggregation.

Fisher Yu, Dequan Wang, Evan Shelhamer, Trevor Darrell

Proceedings of the 6th International Conference on Learning Representations, 2018

Conditional Networks for Few-Shot Semantic Segmentation.

Kate Rakelly, Evan Shelhamer, Trevor Darrell, Alyosha A. Efros, Sergey Levine

Proceedings of the 6th International Conference on Learning Representations, 2018

Deep Layer Aggregation.

Fisher Yu, Dequan Wang, Evan Shelhamer, Trevor Darrell

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

Zero-Shot Visual Imitation.

Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A. Efros, Trevor Darrell

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018

2017

Fully Convolutional Networks for Semantic Segmentation.

Evan Shelhamer, Jonathan Long, Trevor Darrell

IEEE Trans. Pattern Anal. Mach. Intell., 2017

Loss is its own Reward: Self-Supervision for Reinforcement Learning.

Evan Shelhamer, Parsa Mahmoudieh, Max Argus, Trevor Darrell

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Clockwork Convnets for Video Semantic Segmentation.

Evan Shelhamer, Kate Rakelly, Judy Hoffman, Trevor Darrell

Proceedings of the Computer Vision - ECCV 2016 Workshops, 2016

2015

Fine-grained pose prediction, normalization, and recognition.

Ning Zhang, Evan Shelhamer, Yang Gao, Trevor Darrell

CoRR, 2015

Fully Convolutional Multi-Class Multiple Instance Learning.

Deepak Pathak, Evan Shelhamer, Jonathan Long, Trevor Darrell

Proceedings of the 3rd International Conference on Learning Representations, 2015

Scene Intrinsics and Depth from a Single Image.

Evan Shelhamer, Jonathan T. Barron, Trevor Darrell

Proceedings of the 2015 IEEE International Conference on Computer Vision Workshop, 2015

2014

cuDNN: Efficient Primitives for Deep Learning.

Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, Evan Shelhamer

CoRR, 2014

Caffe: Convolutional Architecture for Fast Feature Embedding.

Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross B. Girshick, Sergio Guadarrama, Trevor Darrell

Proceedings of the ACM International Conference on Multimedia, MM '14, Orlando, FL, USA, November 03, 2014