Search Results for author: Frank R. Schmidt

Found 11 papers, 3 papers with code

Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications

no code implementations30 Jun 2020 Eric Wong, Tim Schneider, Joerg Schmitt, Frank R. Schmidt, J. Zico Kolter

Additionally, we show how specific intervals of fuel injection quantities can be targeted to maximize robustness for certain ranges, allowing us to train a virtual sensor for fuel injection which is provably guaranteed to have at most 10. 69% relative error under noise while maintaining 3% relative error on non-adversarial data within normalized fuel injection ranges of 0. 6 to 1. 0.

Adversarial camera stickers: A physical camera-based attack on deep learning systems

1 code implementation21 Mar 2019 Juncheng Li, Frank R. Schmidt, J. Zico Kolter

In this work, we consider an alternative question: is it possible to fool deep classifiers, over all perceived objects of a certain type, by physically manipulating the camera itself?

Wasserstein Adversarial Examples via Projected Sinkhorn Iterations

2 code implementations21 Feb 2019 Eric Wong, Frank R. Schmidt, J. Zico Kolter

In this paper, we propose a new threat model for adversarial attacks based on the Wasserstein distance.

Adversarial Attack Adversarial Defense +4

Scaling provable adversarial defenses

4 code implementations NeurIPS 2018 Eric Wong, Frank R. Schmidt, Jan Hendrik Metzen, J. Zico Kolter

Recent work has developed methods for learning deep network classifiers that are provably robust to norm-bounded adversarial perturbation; however, these methods are currently only possible for relatively small feedforward networks.

Efficient Globally Optimal 2D-to-3D Deformable Shape Matching

no code implementations CVPR 2016 Zorah Lähner, Emanuele Rodolà, Frank R. Schmidt, Michael M. Bronstein, Daniel Cremers

We propose the first algorithm for non-rigid 2D-to-3D shape matching, where the input is a 2D shape represented as a planar curve and a 3D shape represented as a surface; the output is a continuous curve on the surface.

3D Shape Retrieval

Video Segmentation With Just a Few Strokes

no code implementations ICCV 2015 Naveen Shankar Nagaraja, Frank R. Schmidt, Thomas Brox

As the use of videos is becoming more popular in computer vision, the need for annotated video datasets increases.

Motion Segmentation Video Segmentation +1

An Experimental Comparison of Trust Region and Level Sets

no code implementations8 Nov 2013 Lena Gorelick, Ismail BenAyed, Frank R. Schmidt, Yuri Boykov

High-order (non-linear) functionals have become very popular in segmentation, stereo and other computer vision problems.

Fast Trust Region for Segmentation

no code implementations CVPR 2013 Lena Gorelick, Frank R. Schmidt, Yuri Boykov

In this paper we propose a Fast Trust Region (FTR) approach for optimization of segmentation energies with nonlinear regional terms, which are known to be challenging for existing algorithms.

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