Inverting the Generator of a Generative Adversarial Network [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2019 Under review at IEEE ...
Antonia Creswell, Anil Anthony Bharath
openaire +7 more sources
BoostNet: A Boosted Convolutional Neural Network for Image Blind Denoising
IEEE Access, 2021 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
StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for ...
Yunjey Choi +5 more
semanticscholar +1 more source
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments.
Agrim Gupta +4 more
semanticscholar +1 more source
Constrained Generative Adversarial Networks
IEEE Access, 2021 Generative Adversarial Networks (GANs) are a powerful subclass of generative models. Yet, how to effectively train them to reach Nash equilibrium is a challenge.
Xiaopeng Chao +4 more
doaj +1 more source
StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks [PDF]
IEEE International Conference on Computer Vision, 2016 Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing textto- image approaches can roughly reflect the meaning of the given descriptions, but
Han Zhang +6 more
semanticscholar +1 more source
Regularizing Generative Adversarial Networks under Limited Data [PDF]
Computer Vision and Pattern Recognition, 2021 Recent years have witnessed the rapid progress of generative adversarial networks (GANs). However, the success of the GAN models hinges on a large amount of training data.
Hung-Yu Tseng +4 more
semanticscholar +1 more source
Score-Guided Generative Adversarial Networks
Axioms, 2022 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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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network [PDF]
Computer Vision and Pattern Recognition, 2016 Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at ...
C. Ledig +8 more
semanticscholar +1 more source
Geometric Morphometric Data Augmentation Using Generative Computational Learning Algorithms
Applied Sciences, 2020 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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