Results 41 to 50 of about 219,753 (266)
Directional Adversarial Training for Robust Ownership-Based Recommendation System
IEEE Access, 2022 Machine learning algorithms are susceptible to cyberattacks, posing security problems in computer vision, speech recognition, and recommendation systems. So far, researchers have made great strides in adopting adversarial training as a defensive strategy.
Zhefu Wu +3 more
doaj +1 more source
MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks
, 2018 Some recent works revealed that deep neural networks (DNNs) are vulnerable to so-called adversarial attacks where input examples are intentionally perturbed to fool DNNs.
Chen, Yiran +7 more
core +1 more source
Improving Adversarial Robustness via Distillation-Based Purification
Applied Sciences, 2023 Despite the impressive performance of deep neural networks on many different vision tasks, they have been known to be vulnerable to intentionally added noise to input images.
Inhwa Koo, Dong-Kyu Chae, Sang-Chul Lee
doaj +1 more source
Adversarial Removal of Demographic Attributes from Text Data
, 2018 Recent advances in Representation Learning and Adversarial Training seem to succeed in removing unwanted features from the learned representation.
Elazar, Yanai, Goldberg, Yoav
core +1 more source
Regularizing deep networks using efficient layerwise adversarial training
, 2018 Adversarial training has been shown to regularize deep neural networks in addition to increasing their robustness to adversarial examples. However, its impact on very deep state of the art networks has not been fully investigated.
Chellappa, Rama +3 more
core +1 more source
Prior-Guided Adversarial Initialization for Fast Adversarial Training
, 2022 Fast adversarial training (FAT) effectively improves the efficiency of standard adversarial training (SAT). However, initial FAT encounters catastrophic overfitting, i.e.,the robust accuracy against adversarial attacks suddenly and dramatically decreases.
Xiaojun Jia +6 more
openaire +2 more sources
Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models
, 2018 Adversarial learning of probabilistic models has recently emerged as a promising alternative to maximum likelihood. Implicit models such as generative adversarial networks (GAN) often generate better samples compared to explicit models trained by maximum
Dhar, Manik +2 more
core +1 more source
On the Properties of Adversarially-Trained CNNs
CoRR, 2022 Adversarial Training has proved to be an effective training paradigm to enforce robustness against adversarial examples in modern neural network architectures. Despite many efforts, explanations of the foundational principles underpinning the effectiveness of Adversarial Training are limited and far from being widely accepted by the Deep Learning ...
Mattia Carletti +2 more
openaire +2 more sources
Advanced Materials, EarlyView.This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley +1 more source
Increasing the Robustness of Image Quality Assessment Models Through Adversarial Training
TechnologiesThe adversarial robustness of image quality assessment (IQA) models to adversarial attacks is emerging as a critical issue. Adversarial training has been widely used to improve the robustness of neural networks to adversarial attacks, but little in-depth
Anna Chistyakova +6 more
doaj +1 more source