Evan Shelhamer

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 38

Top Topics

Entropy Minimization

Top Venues

ICLR (Workshop)

Publications

Saiyue Lyu, Shadab Shaikh, Frederick Shpilevskiy, Evan Shelhamer, Mathias Lécuyer
Adaptive Randomized Smoothing: Certifying Multi-Step Defences against Adversarial Examples. CoRR (2024)
Francesco Croce, Sylvestre-Alvise Rebuffi, Evan Shelhamer, Sven Gowal
Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts. CoRR (2023)
Francesco Croce, Sylvestre-Alvise Rebuffi, Evan Shelhamer, Sven Gowal
Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts. CVPR (2023)
Zhuang Liu, Hung-Ju Wang, Tinghui Zhou, Zhiqiang Shen, Bingyi Kang, Evan Shelhamer, Trevor Darrell
Exploring Simple and Transferable Recognition-Aware Image Processing. IEEE Trans. Pattern Anal. Mach. Intell. 45 (3) (2023)
Jin Gao, Jialing Zhang, Xihui Liu, Trevor Darrell, Evan Shelhamer, Dequan Wang
Back to the Source: Diffusion-Driven Adaptation to Test-Time Corruption. CVPR (2023)
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
Perceiver IO: A General Architecture for Structured Inputs & Outputs. ICLR (2022)
Jin Gao, Jialing Zhang, Xihui Liu, Trevor Darrell, Evan Shelhamer, Dequan Wang
Back to the Source: Diffusion-Driven Test-Time Adaptation. CoRR (2022)
João Carreira, Skanda Koppula, Daniel Zoran, Adrià Recasens, Catalin Ionescu, Olivier J. Hénaff, Evan Shelhamer, Relja Arandjelovic, Matthew Botvinick, Oriol Vinyals, Karen Simonyan, Andrew Zisserman, Andrew Jaegle
Hierarchical Perceiver. CoRR (2022)
Skanda Koppula, Yazhe Li, Evan Shelhamer, Andrew Jaegle, Nikhil Parthasarathy, Relja Arandjelovic, João Carreira, Olivier J. Hénaff
Where Should I Spend My FLOPS? Efficiency Evaluations of Visual Pre-training Methods. CoRR (2022)
Olivier J. Hénaff, Skanda Koppula, Evan Shelhamer, Daniel Zoran, Andrew Jaegle, Andrew Zisserman, João Carreira, Relja Arandjelovic
Object discovery and representation networks. CoRR (2022)
Zhuang Liu, Zhiqiu Xu, Hung-Ju Wang, Trevor Darrell, Evan Shelhamer
Anytime Dense Prediction with Confidence Adaptivity. ICLR (2022)
Olivier J. Hénaff, Skanda Koppula, Evan Shelhamer, Daniel Zoran, Andrew Jaegle, Andrew Zisserman, João Carreira, Relja Arandjelovic
Object Discovery and Representation Networks. ECCV (27) (2022)
Francesco Croce, Sven Gowal, Thomas Brunner, Evan Shelhamer, Matthias Hein, A. Taylan Cemgil
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses. CoRR (2022)
Francesco Croce, Sven Gowal, Thomas Brunner, Evan Shelhamer, Matthias Hein, A. Taylan Cemgil
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses. ICML (2022)
Zhuang Liu, Trevor Darrell, Evan Shelhamer
Confidence Adaptive Anytime Pixel-Level Recognition. CoRR (2021)
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno A. Olshausen, Trevor Darrell
Tent: Fully Test-Time Adaptation by Entropy Minimization. ICLR (2021)
Dequan Wang, An Ju, Evan Shelhamer, David A. Wagner, Trevor Darrell
Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks. CoRR (2021)
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
Perceiver IO: A General Architecture for Structured Inputs & Outputs. CoRR (2021)
Dequan Wang, Shaoteng Liu, Sayna Ebrahimi, Evan Shelhamer, Trevor Darrell
On-target Adaptation. CoRR (2021)
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno A. Olshausen, Trevor Darrell
Fully Test-time Adaptation by Entropy Minimization. CoRR (2020)
Mark Hamilton, Evan Shelhamer, William T. Freeman
It Is Likely That Your Loss Should be a Likelihood. CoRR (2020)
Kelsey R. Allen, Evan Shelhamer, Hanul Shin, Joshua B. Tenenbaum
Infinite Mixture Prototypes for Few-shot Learning. ICML (2019)
Evan Shelhamer, Dequan Wang, Trevor Darrell
Blurring the Line Between Structure and Learning to Optimize and Adapt Receptive Fields. CoRR (2019)
Dequan Wang, Evan Shelhamer, Bruno A. Olshausen, Trevor Darrell
Dynamic Scale Inference by Entropy Minimization. CoRR (2019)
Kelsey R. Allen, Evan Shelhamer, Hanul Shin, Joshua B. Tenenbaum
Infinite Mixture Prototypes for Few-Shot Learning. CoRR (2019)
Kate Rakelly, Evan Shelhamer, Trevor Darrell, Alyosha A. Efros, Sergey Levine
Conditional Networks for Few-Shot Semantic Segmentation. ICLR (Workshop) (2018)
Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A. Efros, Trevor Darrell
Zero-Shot Visual Imitation. ICLR (2018)
Fisher Yu, Dequan Wang, Evan Shelhamer, Trevor Darrell
Deep Layer Aggregation. CVPR (2018)
Kate Rakelly, Evan Shelhamer, Trevor Darrell, Alexei A. Efros, Sergey Levine
Few-Shot Segmentation Propagation with Guided Networks. CoRR (2018)
Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A. Efros, Trevor Darrell
Zero-Shot Visual Imitation. CoRR (2018)
Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A. Efros, Trevor Darrell
Zero-Shot Visual Imitation. CVPR Workshops (2018)
Fisher Yu, Dequan Wang, Evan Shelhamer, Trevor Darrell
Learning Rich Image Representation with Deep Layer Aggregation. ICLR (Workshop) (2018)
Evan Shelhamer, Jonathan Long, Trevor Darrell
Fully Convolutional Networks for Semantic Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39 (4) (2017)
Evan Shelhamer, Parsa Mahmoudieh, Max Argus, Trevor Darrell
Loss is its own Reward: Self-Supervision for Reinforcement Learning. ICLR (Workshop) (2017)
Evan Shelhamer, Kate Rakelly, Judy Hoffman, Trevor Darrell
Clockwork Convnets for Video Semantic Segmentation. ECCV Workshops (3) (2016)
Evan Shelhamer, Kate Rakelly, Judy Hoffman, Trevor Darrell
Clockwork Convnets for Video Semantic Segmentation. CoRR (2016)
Evan Shelhamer, Parsa Mahmoudieh, Max Argus, Trevor Darrell
Loss is its own Reward: Self-Supervision for Reinforcement Learning. CoRR (2016)
Evan Shelhamer, Jonathan Long, Trevor Darrell
Fully Convolutional Networks for Semantic Segmentation. CoRR (2016)
Ning Zhang, Evan Shelhamer, Yang Gao, Trevor Darrell
Fine-grained pose prediction, normalization, and recognition. CoRR (2015)
Evan Shelhamer, Jonathan T. Barron, Trevor Darrell
Scene Intrinsics and Depth from a Single Image. ICCV Workshops (2015)
Jonathan Long, Evan Shelhamer, Trevor Darrell
Fully convolutional networks for semantic segmentation. CVPR (2015)
Deepak Pathak, Evan Shelhamer, Jonathan Long, Trevor Darrell
Fully Convolutional Multi-Class Multiple Instance Learning. ICLR (Workshop) (2015)
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross B. Girshick, Sergio Guadarrama, Trevor Darrell
Caffe: Convolutional Architecture for Fast Feature Embedding. ACM Multimedia (2014)
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross B. Girshick, Sergio Guadarrama, Trevor Darrell
Caffe: Convolutional Architecture for Fast Feature Embedding. CoRR (2014)
Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, Evan Shelhamer
cuDNN: Efficient Primitives for Deep Learning. CoRR (2014)
Jonathan Long, Evan Shelhamer, Trevor Darrell
Fully Convolutional Networks for Semantic Segmentation. CoRR (2014)