参考资料 [1] Salimans, Tim, et al. "Improved techniques for training gans." Advances in neural information processing systems. 2016.
原文 We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. We focus on two applications of GANs:semi-supervised learning, and the generation of images that humans find visually realistic. Unlike most work on...
GANs的一些技巧(ImprovedTechniquesforTrainingGANs)2016 原文链接:https://arxiv.org/pdf/1606.03498v1.pdf 条件生成...生成模型样本, 优化目标是达到纳什均衡, 使生成器估测到数据样本的分布.GAN目前在图像和视觉领域得到了广泛的研究和应用, 已经可以生成数字和人脸等物体对象, 构成各种逼真的室内外场景, 从 ...
Efficient Data Augmentation Techniques for Improved Classification in Limited Data Set of Oral Squamous Cell Carcinoma (GANs)are very powerful techniques to augment training data as new samples are created.This technique helps the classification models to increase their ... W Alosaimi,MI Uddin - 工程...
《Improved Techniques for Training GANs》T Salimans, I Goodfellow, W Zaremba, V Cheung, A Radford, X Chen [OpenAI] (2016) http://t.cn/R5X5mXI GitHub:http://t.cn/R5X5mXf
特征的平均值和标准差使用参考样本的batch进行计算。 然后,使用这些统计的信息对两个batch的特征进行标准化处理。 但这方法的开销很大,所以只对生成器使用了Virtual batch normalization。 参考:Improved Techniques for Training GANs翻译与理解
Improved Techniques for Training GANs 训练GANs 其实是一个找纳什均衡的问题。但是找高维连续非凸问题的纳什均衡点是很困难的。而且,在GAN的训练中我们通常是通过梯度下降法来最小化代价函数的,而不是去找纳什均衡点,所以我们经常会碰到无法收敛的情况。
ganstechniquestrainingimproved人工智能nash ImprovedTechniquesforTrainingGANs TimSalimans tim@openai IanGoodfellow ian@openai WojciechZaremba woj@openai VickiCheung vicki@openai AlecRadford alec.radford@gmail XiChen peter@openai Abstract Wepresentavarietyofnewarchitecturalfeaturesandtrainingproceduresthatwe applytoth...
Improved Techniques for Training Single-Image GANs 来自 Semantic Scholar 喜欢 0 阅读量: 672 作者:T Hinz,M Fisher,O Wang,S Wermter 摘要: Recently there has been an interest in the potential of learning generative models from a single image, as opposed to from a large dataset. This task is...
A Tensorflow implementation of Semi-supervised Learning Generative Adversarial Networks (NIPS 2016: Improved Techniques for Training GANs). - clvrai/SSGAN-Tensorflow