main webpage W Topic Astrophysics arXiv (Cornell University) Distance Guided Generative Adversarial Network for Explainable Binary Classifications 2023 Despite the potential benefits of data augmentation for mitigating the data insufficiency, traditional augmentation methods primarily rely on the prior intra-domain knowledge. On the other hand, advanced generative adversarial networks (GANs) generate inter-d… Article Open Astrophysics Open Article Page

Astrophysics

Subfield of astronomy Astrophysics is a science that employs the methods and principles of physics and chemistry in the study of astronomical objects and phenomena. As one of the founders of the discipline, James Keeler, said, astrophysics "seeks to ascertain the nature of the heavenly bodies, rather than their positions or motions in space— what they are, rather than where they are", which is studied in celestial mechanics. Among the subjects studied are the Sun (solar physics), other stars, galaxies, extrasolar planets, the interstellar medium, and the cosmic microwave background. Emissions from these objects are examined across all parts of the electromagnetic spectrum, and the properties examined include luminosity, density, temperature, and chemical composition. Critical Symbolic Virtual Narrative Open Astrophysics Open Article Page

Exploring foci of: arXiv (Cornell University) Distance Guided Generative Adversarial Network for Explainable Binary Classifications 2023 Despite the potential benefits of data augmentation for mitigating the data insufficiency, traditional augmentation methods primarily rely on the prior intra-domain knowledge. On the other hand, advanced generative adversarial networks (GANs) generate inter-domain samples with limited variety. These previous methods make limited contributions to describing the decision boundaries for binary classification. In this paper, we propose a distance guided GAN (DisGAN) which controls the variation degrees of generated sa… Open Article Page

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Explore Astrophysics W Topic Astrophysics arXiv (Cornell University) Distance Guided Generative Adversarial Network for Explainable Binary Classifications 2023 Despite the potential benefits of data augmentation for mitigating the data insufficiency, traditional augmentation methods primarily rely on the prior intra-domain knowledge. On the other hand, advanced generative adversarial networks (GANs) generate inter-d… Article Critical Symbolic Virtual Narrative Open Astrophysics Open Article Page