research
∙
03/09/2023
Identification of Systematic Errors of Image Classifiers on Rare Subgroups
Despite excellent average-case performance of many image classifiers, th...
share
research
∙
09/13/2022
Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation
Adversarial patch attacks are an emerging security threat for real world...
research
∙
02/15/2022
Neural Architecture Search for Dense Prediction Tasks in Computer Vision
The success of deep learning in recent years has lead to a rising demand...
research
∙
11/02/2021
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks
Adversarial attacks based on randomized search schemes have obtained sta...
research
∙
07/08/2021
Bag of Tricks for Neural Architecture Search
While neural architecture search methods have been successful in previou...
research
∙
06/28/2021
Test-Time Adaptation to Distribution Shift by Confidence Maximization and Input Transformation
Deep neural networks often exhibit poor performance on data that is unli...
research
∙
04/20/2021
Does enhanced shape bias improve neural network robustness to common corruptions?
Convolutional neural networks (CNNs) learn to extract representations of...
research
∙
02/08/2021
Efficient Certified Defenses Against Patch Attacks on Image Classifiers
Adversarial patches pose a realistic threat model for physical world att...
research
∙
01/27/2021
Meta Adversarial Training
Recently demonstrated physical-world adversarial attacks have exposed vu...
research
∙
10/12/2020
Increasing the Robustness of Semantic Segmentation Models with Painting-by-Numbers
For safety-critical applications such as autonomous driving, CNNs have t...
research
∙
10/03/2020
Adversarial and Natural Perturbations for General Robustness
In this paper we aim to explore the general robustness of neural network...
research
∙
11/25/2019
Meta-Learning of Neural Architectures for Few-Shot Learning
The recent progress in neural architectures search (NAS) has allowed sca...
research
∙
10/15/2019
Understanding Misclassifications by Attributes
In this paper, we aim to understand and explain the decisions of deep ne...
research
∙
04/17/2019
Interpreting Adversarial Examples with Attributes
Deep computer vision systems being vulnerable to imperceptible and caref...
research
∙
12/10/2018
Defending against Universal Perturbations with Shared Adversarial Training
Classifiers such as deep neural networks have been shown to be vulnerabl...
research
∙
08/16/2018
Neural Architecture Search: A Survey
Deep Learning has enabled remarkable progress over the last years on a v...
research
∙
05/31/2018
Scaling provable adversarial defenses
Recent work has developed methods for learning deep network classifiers ...
research
∙
04/24/2018
Multi-objective Architecture Search for CNNs
Architecture search aims at automatically finding neural architectures t...
research
∙
11/13/2017
Simple And Efficient Architecture Search for Convolutional Neural Networks
Neural networks have recently had a lot of success for many tasks. Howev...
research
∙
04/19/2017
Universal Adversarial Perturbations Against Semantic Image Segmentation
While deep learning is remarkably successful on perceptual tasks, it was...
research
∙
03/03/2017
Adversarial Examples for Semantic Image Segmentation
Machine learning methods in general and Deep Neural Networks in particul...
research
∙
02/14/2017
On Detecting Adversarial Perturbations
Machine learning and deep learning in particular has advanced tremendous...
research
∙
02/02/2016
Minimum Regret Search for Single- and Multi-Task Optimization
We propose minimum regret search (MRS), a novel acquisition function for...
research
∙
11/13/2015
