Publications related to Corruption

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

Amir Roshan Zamir, Oguzhan Fatih Kar, Shuqing Teresa Yeo, Zahra Sodagar

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, Guillermo Ortiz Jimenez, Apostolos Modas

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, Yuan He

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, Oguzhan Fatih Kar, Andrei Atanov, Shuqing Teresa Yeo

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

On the effectiveness of adversarial training against common corruptions

Nicolas Henri Bernard Flammarion, Maksym Andriushchenko, Klim Kireev

The literature on robustness towards common corruptions shows no consensus on whether adversarial training can improve the performance in this setting. First, we show that, when used with an appropriately selected perturbation radius, ℓp adversarial traini ...

2021

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 ...

ELSEVIER SCIENCE SA2021

Pretraining boosts out-of-domain robustness for pose estimation

Alexander Mathis, Mackenzie Mathis, Steffen Schneider, Matthias Bethge, Thomas Ray Biasi, Mert Yüksekgönül

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

Leader corruption depends on power and testosterone

Samuel Bendahan, John Antonakis

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

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Corruption

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

PRIME: A Few Primitives Can Boost Robustness to Common Corruptions

Towards Robust Vision Transformer

3D Common Corruptions and Data Augmentation

On the effectiveness of adversarial training against common corruptions

Kalman filter density reconstruction in ICRH discharges on ASDEX Upgrade

Pretraining boosts out-of-domain robustness for pose estimation

Block Oriented Programming: Automating Data-Only Attacks

Leader corruption depends on power and testosterone

Strong Privacy for RFID Systems from Plaintext-Aware Encryption

Graph Chatbot

Chat with Graph Search

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

3D Common Corruptions and Data Augmentation

PRIME: A Few Primitives Can Boost Robustness to Common Corruptions

Towards Robust Vision Transformer

On the effectiveness of adversarial training against common corruptions

Pretraining boosts out-of-domain robustness for pose estimation

Block Oriented Programming: Automating Data-Only Attacks

Strong Privacy for RFID Systems from Plaintext-Aware Encryption

Leader corruption depends on power and testosterone

Kalman filter density reconstruction in ICRH discharges on ASDEX Upgrade