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Concept

Corruption

Publications associées (9)

Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback

Amir Roshan Zamir, Shuqing Teresa Yeo, Oguzhan Fatih Kar

We propose a method for adapting neural networks to distribution shifts at test-time. In contrast to training-time robustness mechanisms that attempt to anticipate and counter the shift, we create a closed-loop system and make use of test-time feedback sig ...

Ieee Computer Soc2023

PRIME: A Few Primitives Can Boost Robustness to Common Corruptions

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Apostolos Modas, Guillermo Ortiz Jimenez

Despite their impressive performance on image classification tasks, deep networks have a hard time generalizing to unforeseen corruptions of their data. To fix this vulnerability, prior works have built complex data augmentation strategies, combining multi ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Towards Robust Vision Transformer

Shaokai Ye

Recent advances on Vision Transformer (ViT) and its improved variants have shown that self-attention-based networks surpass traditional Convolutional Neural Networks (CNNs) in most vision tasks. However, existing ViTs focus on the standard accuracy and com ...

IEEE COMPUTER SOC2022

3D Common Corruptions and Data Augmentation

Amir Roshan Zamir, Shuqing Teresa Yeo, Oguzhan Fatih Kar, Andrei Atanov

We introduce a set of image transformations that can be used as corruptions to evaluate the robustness of models as well as data augmentation mechanisms for training neural networks. The primary distinction of the proposed transformations is that, unlike e ...

IEEE COMPUTER SOC2022

Kalman filter density reconstruction in ICRH discharges on ASDEX Upgrade

Federico Alberto Alfredo Felici, Emiliano Fable

Plasma density is one of the key quantities that need to be controlled in real-time as it scales directly with fusion power and, if left uncontrolled, density limits can be reached leading to a disruption. On ASDEX Upgrade (AUG), the real-time measurements ...

2021

Pretraining boosts out-of-domain robustness for pose estimation

Alexander Mathis, Mackenzie Mathis, Matthias Bethge, Steffen Schneider

Neural networks are highly effective tools for pose estimation. However, as in other computer vision tasks, robustness to out-of-domain data remains a challenge, especially for small training sets that are common for real-world applications. Here, we probe ...

IEEE COMPUTER SOC2021

Block Oriented Programming: Automating Data-Only Attacks

Mathias Josef Payer

With the widespread deployment of Control-Flow Integrity (CFI), control-flow hijacking attacks, and consequently code reuse attacks, are significantly more difficult. CFI limits control flow to well-known locations, severely restricting arbitrary code exec ...

ASSOC COMPUTING MACHINERY2018

Fast Robust PCA on Graphs

Pierre Vandergheynst, Gilles Puy, Nathanaël Perraudin, Vasilis Kalofolias, Nauman Shahid

Mining useful clusters from high dimensional data has received significant attention of the computer vision and pattern recognition community in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome the c ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Leader corruption depends on power and testosterone

Samuel Bendahan

We used incentivized experimental games to manipulate leader power - the number of followers and the discretion leaders had to enforce their will. Leaders had complete autonomy in deciding payouts to themselves and their followers. Although leaders could m ...

Elsevier Science Inc2015