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...
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
参考资料 [1] Salimans, Tim, et al. "Improved techniques for training gans." Advances in neural information processing systems. 2016.
Semi-supervised learning 对于标准分类网络,我们的输出是k个类别对应的概率。在这里我们借助分类网络进行无监督学习。我们把生成样本当成第K+1类,用x属于k+1类的概率当做x为假的概率。 loss function for training the classifier: 这里的无监督过程相当于训练一个GAN。 实验结果 左边feature matching,右边minibatch ...
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 ...
1.我们以不同的学习率并行训练几个阶段,并且可以权衡生成图像的方差与它们与原始训练图像的一致性。 2.我们不在中间阶段生成图像,而是将特征从一个阶段直接传播到下一个阶段。 3.我们改进了多阶段训练的重新缩放方法,使我们能够在更少的阶段进行训练。
To tackle these challenges, we present improved techniques for consistency training, where consistency models learn directly from data without distillation. We delve into the theory behind consistency training and identify a previously overlooked flaw, which we address by eliminating Exponential Moving ...
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 一篇在SinGAN上改进的论文,特点是更可控,训练更快20-30min。代码开源:ConSinGAN 主要贡献: 并行训练多个阶段 没有在中间阶段生成图像,而是传播特征 提升训练过程的尺度步骤,比之前需要的训练阶段更少 利用微调来应用多个应用领域...
《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