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Robust Proxy: Improving Adversarial Robustness by Robust Proxy Learning [PDF]
IEEE Transactions on Information Forensics and Security, 2023 Recently, it has been widely known that deep neural networks are highly vulnerable and easily broken by adversarial attacks. To mitigate the adversarial vulnerability, many defense algorithms have been proposed. Recently, to improve adversarial robustness, many works try to enhance feature representation by imposing more direct supervision on the ...
Hong Joo Lee, Yong Man Ro
arxiv +3 more sources
The Robustness of Quintessence
Physical Review D, 1999 Recent observations seem to suggest that our Universe is accelerating implying that it is dominated by a fluid whose equation of state is negative. Quintessence is a possible explanation. In particular, the concept of tracking solutions permits to adress
A. G. Riess +31 more
core +2 more sources
Robustness and Generalization [PDF]
Machine Learning, 2010 We derive generalization bounds for learning algorithms based on their robustness: the property that if a testing sample is "similar" to a training sample, then the testing error is close to the training error.
A. Ben-Tal +57 more
core +4 more sources
On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective [PDF]
IEEE Data Engineering Bulletin, 2023 ChatGPT is a recent chatbot service released by OpenAI and is receiving increasing attention over the past few months. While evaluations of various aspects of ChatGPT have been done, its robustness, i.e., the performance to unexpected inputs, is still ...
Jindong Wang +12 more
semanticscholar +1 more source
Understanding Robustness of Transformers for Image Classification [PDF]
IEEE International Conference on Computer Vision, 2021 Deep Convolutional Neural Networks (CNNs) have long been the architecture of choice for computer vision tasks. Recently, Transformer-based architectures like Vision Transformer (ViT) have matched or even surpassed ResNets for image classification ...
Srinadh Bhojanapalli +5 more
semanticscholar +1 more source
Recent Advances in Adversarial Training for Adversarial Robustness [PDF]
International Joint Conference on Artificial Intelligence, 2021 Adversarial training is one of the most effective approaches for deep learning models to defend against adversarial examples. Unlike other defense strategies, adversarial training aims to enhance the robustness of models intrinsically.
Tao Bai +4 more
semanticscholar +1 more source
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization [PDF]
IEEE International Conference on Computer Vision, 2020 We introduce four new real-world distribution shift datasets consisting of changes in image style, image blurriness, geographic location, camera operation, and more. With our new datasets, we take stock of previously proposed methods for improving out-of-
Dan Hendrycks +12 more
semanticscholar +1 more source
On the Adversarial Robustness of Robust Estimators [PDF]
IEEE Transactions on Information Theory, 2020 Motivated by recent data analytics applications, we study the adversarial robustness of robust estimators. Instead of assuming that only a fraction of the data points are outliers as considered in the classic robust estimation setup, in this paper, we consider an adversarial setup in which an attacker can observe the whole dataset and can modify all ...
Lifeng Lai, Erhan Bayraktar
openaire +3 more sources
Understanding The Robustness in Vision Transformers [PDF]
International Conference on Machine Learning, 2022 Recent studies show that Vision Transformers(ViTs) exhibit strong robustness against various corruptions. Although this property is partly attributed to the self-attention mechanism, there is still a lack of systematic understanding.
Daquan Zhou +6 more
semanticscholar +1 more source
Indoor–Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches
Computers, 2021 Communication networks are managed more and more by using artificial intelligence. Anomaly detection, network monitoring and user behaviour are areas where machine learning offers advantages over more traditional methods.
Frank Phillipson +2 more
doaj +1 more source