Deep learning approach for defective spot welds classification using small and class-imbalanced datasets
W Dai, D Li, D Tang, H Wang, Y Peng - Neurocomputing, 2022 - Elsevier
Availability of large-scale annotated and class-balanced datasets is of great importance for
deep learning based computer vision tasks like spot welds detection. However, it is …
deep learning based computer vision tasks like spot welds detection. However, it is …
Min-max optimization without gradients: Convergence and applications to black-box evasion and poisoning attacks
In this paper, we study the problem of constrained min-max optimization in a black-box
setting, where the desired optimizer cannot access the gradients of the objective function but …
setting, where the desired optimizer cannot access the gradients of the objective function but …
Lights and shadows in evolutionary deep learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challenges
Much has been said about the fusion of bio-inspired optimization algorithms and Deep
Learning models for several purposes: from the discovery of network topologies and …
Learning models for several purposes: from the discovery of network topologies and …
COEGAN: evaluating the coevolution effect in generative adversarial networks
Generative adversarial networks (GAN) present state-of-the-art results in the generation of
samples following the distribution of the input dataset. However, GANs are difficult to train …
samples following the distribution of the input dataset. However, GANs are difficult to train …
Coevolutionary generative adversarial networks for medical image augumentation at scale
Medical image processing can lack images for diagnosis. Generative Adversarial Networks
(GANs) provide a method to train generative models for data augmentation. Synthesized …
(GANs) provide a method to train generative models for data augmentation. Synthesized …
The application of evolutionary computation in generative adversarial networks (GANs): a systematic literature survey
Y Wang, Q Zhang, GG Wang, H Cheng - Artificial Intelligence Review, 2024 - Springer
As a subfield of deep learning (DL), generative adversarial networks (GANs) have produced
impressive generative results by applying deep generative models to create synthetic data …
impressive generative results by applying deep generative models to create synthetic data …
Spatial evolutionary generative adversarial networks
Generative adversary networks (GANs) suffer from training pathologies such as instability
and mode collapse. These pathologies mainly arise from a lack of diversity in their …
and mode collapse. These pathologies mainly arise from a lack of diversity in their …
Bi-EvoGAN: Bi-level Evolutionary Approach for Generative Adversarial Networks
Abstract Generative Adversarial Networks (GANs) are an adversarial approach to generative
modeling using deep learning methods, which have become one of the most relevant topics …
modeling using deep learning methods, which have become one of the most relevant topics …
CDE-GAN: Cooperative dual evolution-based generative adversarial network
Generative adversarial networks (GANs) have been a popular deep generative model for
real-world applications. Despite many recent efforts on GANs that have been contributed …
real-world applications. Despite many recent efforts on GANs that have been contributed …
[HTML][HTML] Semi-supervised generative adversarial networks with spatial coevolution for enhanced image generation and classification
Labeling images for classification can be expensive. Semi-Supervised Learning (SSL)
Generative Adversarial Network (GAN) methods train good classifiers with a few labeled …
Generative Adversarial Network (GAN) methods train good classifiers with a few labeled …