Results 21 to 30 of about 39,370 (305)
Inverting the Generator of a Generative Adversarial Network [PDF]
Under review at IEEE ...
Antonia Creswell, Anil Anthony Bharath
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Triple Generative Adversarial Networks [PDF]
We propose a unified game-theoretical framework to perform classification and conditional image generation given limited supervision. It is formulated as a three-player minimax game consisting of a generator, a classifier and a discriminator, and therefore is referred to as Triple Generative Adversarial Network (Triple-GAN).
Chongxuan Li +4 more
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Augmenting Generative Adversarial Networks for Speech Emotion Recognition [PDF]
Generative adversarial networks (GANs) have shown potential in learning emotional attributes and generating new data samples. However, their performance is usually hindered by the unavailability of larger speech emotion recognition (SER) data.
Raja Jurdak +11 more
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BoostNet: A Boosted Convolutional Neural Network for Image Blind Denoising
Deep convolutional neural networks and generative adversarial networks currently attracted the attention of researchers because it is more effective than conventional representation-based methods.
Duc My Vo +3 more
doaj +1 more source
Fuzzy Generative Adversarial Networks
Generative Adversarial Networks (GANs) are well-known tools for data generation and semi-supervised classification. GANs, with less labeled data, outperform Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) in classification across various tasks, this shows promise for developing GANs capable of trespassing into the domain of semi ...
Ryan Nguyen +2 more
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Self-Sparse Generative Adversarial Networks
Generative adversarial networks (GANs) are an unsupervised generative model that learns data distribution through adversarial training. However, recent experiments indicated that GANs are difficult to train due to the requirement of optimization in the ...
Wenliang Qian +3 more
doaj +1 more source
Generative adversarial networks and diffusion models in material discovery
The idea of materials discovery has excited and perplexed research scientists for centuries. Several different methods have been employed to find new types of materials, ranging from the arbitrary replacement of atoms in a crystal structure to advanced ...
Michael, Alverson +5 more
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Score-Guided Generative Adversarial Networks
We propose a generative adversarial network (GAN) that introduces an evaluator module using pretrained networks. The proposed model, called a score-guided GAN (ScoreGAN), is trained using an evaluation metric for GANs, i.e., the Inception score, as a ...
Minhyeok Lee, Junhee Seok
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Numerical Analysis of Bio-signal Using Generative Adversarial Networks [PDF]
In this decade, it is not necessary to have technical knowledge for the investment since the automatic algorithms to sell/buy investment destination have been developed with artificial intelligence (AI).
Ono, Rentarou +9 more
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Geometric Morphometric Data Augmentation Using Generative Computational Learning Algorithms
The fossil record is notorious for being incomplete and distorted, frequently conditioning the type of knowledge that can be extracted from it. In many cases, this often leads to issues when performing complex statistical analyses, such as classification
Lloyd A. Courtenay +1 more
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