Results 41 to 50 of about 215,342 (268)

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, Jain, Arpit, Lim, Ser Nam, Sankaranarayanan, Swami   +3 more
core   +1 more source

A3T: Adversarially Augmented Adversarial Training

, 2018
accepted for an oral presentation in Machine Deception Workshop, NIPS ...
Erraqabi, Akram, Baratin, Aristide, Bengio, Yoshua, Lacoste-Julien, Simon   +3 more
openaire   +2 more sources

Multi-Class Triplet Loss With Gaussian Noise for Adversarial Robustness

IEEE Access, 2020
Deep Neural Networks (DNNs) classifiers performance degrades under adversarial attacks, such attacks are indistinguishably perturbed relative to the original data.
Benjamin Appiah, Edward Y. Baagyere, Kwabena Owusu-Agyemang, Zhiguang Qin, Muhammed Amin Abdullah   +4 more
doaj   +1 more source

A Robust Method to Protect Text Classification Models against Adversarial Attacks

Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022
Text classification is one of the main tasks in natural language processing. Recently, adversarial attacks have shown a substantial negative impact on neural network-based text classification models. There are few defenses to strengthen model predictions
BALA MALLIKARJUNARAO GARLAPATI, Ajeet Kumar Singh, Srinivasa Rao Chalamala   +2 more
doaj   +1 more source

Active Learning‐Guided Accelerated Discovery of Ultra‐Efficient High‐Entropy Thermoelectrics

Advanced Materials, EarlyView.
An active learning framework is introduced for the accelerated discovery of high‐entropy chalcogenides with superior thermoelectric performance. Only 80 targeted syntheses, selected from 16206 possible combinations, led to three high‐performance compositions, demonstrating the remarkable efficiency of data‐driven guidance in experimental materials ...
Hanhwi Jang, Wooseok Lee, Hwa‐Jung Kim, Sohyang Cha, Hosun Shin, Won Bo Lee, Min‐Wook Oh, Yeon Sik Jung, YongJoo Kim   +8 more
wiley   +1 more source

Increasing the Robustness of Image Quality Assessment Models Through Adversarial Training

Technologies
The 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, Anastasia Antsiferova, Maksim Khrebtov, Sergey Lavrushkin, Konstantin Arkhipenko, Dmitriy Vatolin, Denis Turdakov   +6 more
doaj   +1 more source

Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders

, 2018
Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation.
D He, JB Tenenbaum, L Maaten van der, S Hinterstoisser, Y. Lecun   +4 more
core   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

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

Adversarial Discriminative Domain Adaptation

, 2017
Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains. They also can improve recognition despite the presence of domain shift or dataset bias: several adversarial ...
Darrell, Trevor, Hoffman, Judy, Saenko, Kate, Tzeng, Eric   +3 more
core   +1 more source

From the Discovery of the Giant Magnetocaloric Effect to the Development of High‐Power‐Density Systems

Advanced Materials Technologies, EarlyView.
The article overviews past and current efforts on caloric materials and systems, highlighting the contributions of Ames National Laboratory to the field. Solid‐state caloric heat pumping is an innovative method that can be implemented in a wide range of cooling and heating applications.
Agata Czernuszewicz, Yaroslav Mudryk, Jun Cui, Lucas Griffith, Duane D. Johnson, Julie Slaughter   +5 more
wiley   +1 more source