GitHub - tamarott/SinGAN: Official pytorch implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image"You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert
Synthesizing Tabular Data using Generative Adversarial Networks (preprint) 読んだ - 糞糞糞ネット弁慶
[1811.11264] Synthesizing Tabular Data using Generative Adversarial Networks] GAN を使って表形式のデータを生成する論文は既に読んだわけですが，その発展形． 著者らによる実装も公開されており(DAI-Lab/TGAN: Generative adversarial training for synthesizing tabular data)，実装を試した人もいる(テーブルデータ向けのGAN（TGAN）で、titanicのデータを増やす - u++の備忘録)． 前述した tableGAN との違いは CNN を用いずに LSTM を用いていること，交差エントロピーを用いるのではなく KL divergence を使って周辺分布を学習していることの二点． データ変換 データが 個の連続値の変数 と 個の離散値の変
[1812.04948] A Style-Based Generator Architecture for Generative Adversarial Networks
We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific
CariGAN
Facial caricature is an art form of drawing faces in an exaggerated way to convey humor or sarcasm. In this paper, we propose the first Generative Adversarial Network (GAN) for unpaired photo-to-caricature translation, which we call "CariGANs". It explicitly models geometric exaggeration and appearance stylization using two components: CariGeoGAN, which only models the geometry-to-geometry transfo
CVPR 2018 Tutorial on GANs
Location: Room 355 DEF, Calvin L. Rampton Salt Palace Convention Center in Salt Lake City, Utah Generative adversarial networks (GANs) have been at the forefront of research on generative models in the last couple of years. GANs have been used for image generation, image processing, image synthesis from captions, image editing, visual domain adaptation, data generation for visual recognition, and
Are GANs created equal? A large-scale study
Generative adversarial networks (GAN) are a powerful subclass of generative models. Despite a very rich research activity leading to numerous interesting GAN algorithms, it is still very hard to assess which algorithm(s) perform better than others. We conduct a neutral, multi-faceted large-scale empirical study on state-of-the art models and evaluation measures. We find that most models can reach
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