Search Results for author: Nathan Silberman

Found 8 papers, 4 papers with code

Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion

1 code implementation CVPR 2019 Ryutaro Tanno, Ardavan Saeedi, Swami Sankaranarayanan, Daniel C. Alexander, Nathan Silberman

We provide a theoretical argument as to how the regularization is essential to our approach both for the case of single annotator and multiple annotators.

Image Classification

ExplainGAN: Model Explanation via Decision Boundary Crossing Transformations

no code implementations ECCV 2018 Pouya Samangouei, Ardavan Saeedi, Liam Nakagawa, Nathan Silberman

We introduce a new method for interpreting computer vision models: visually perceptible, decision-boundary crossing transformations.

The Devil is in the Decoder: Classification, Regression and GANs

1 code implementation18 Jul 2017 Zbigniew Wojna, Vittorio Ferrari, Sergio Guadarrama, Nathan Silberman, Liang-Chieh Chen, Alireza Fathi, Jasper Uijlings

Many machine vision applications, such as semantic segmentation and depth prediction, require predictions for every pixel of the input image.

Boundary Detection Depth Estimation +4

Domain Separation Networks

5 code implementations NeurIPS 2016 Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, Dumitru Erhan

However, by focusing only on creating a mapping or shared representation between the two domains, they ignore the individual characteristics of each domain.

Domain Generalization Unsupervised Domain Adaptation

Im2Calories: Towards an Automated Mobile Vision Food Diary

no code implementations ICCV 2015 Austin Meyers, Nick Johnston, Vivek Rathod, Anoop Korattikara, Alex Gorban, Nathan Silberman, Sergio Guadarrama, George Papandreou, Jonathan Huang, Kevin P. Murphy

We present a system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories.

Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models

no code implementations NeurIPS 2009 Ryan Mcdonald, Mehryar Mohri, Nathan Silberman, Dan Walker, Gideon S. Mann

Training conditional maximum entropy models on massive data requires significant time and computational resources.

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