因为1x1卷积的参数W是基于其在每一个位置得到的累积误差,所以其(变换参数)是由整张图的所有变量点决定的。注意LU分解中的permutation矩阵和permutation操作没有关系。 基于流的生成式模型再概念上对于易解准确对数似然,准确隐变量推断的易解性,和训练与合成的并行性。在本文中我们使用Glow,一个简单的生成式流类,使用...
Fundamental Architecture 这篇文章提出的flow结构主要包含三个(Actnorm,Invertible 1x1 convolution和Affine coupling layer),其中一个flow step的结构如下: Table 1中列出了三个基本组成结构的运算细节,对应的可逆运算及log-determinant(计算训练数据的对数似然)值: Actnorm BN(Batch Normalization)操作带来的noise与batch ...
In this paper we propose Glow, a simple type of generative flow using an invertible 1x1 convolution. Using our method we demonstrate a significant improvement in log-likelihood on standard benchmarks. Perhaps most strikingly, we demonstrate that a generative model optimized towards the plain log-...
Status:Archive (code is provided as-is, no updates expected) Glow Code for reproducing results in"Glow: Generative Flow with Invertible 1x1 Convolutions" To use pretrained CelebA-HQ model, make your own manipulation vectors and run our interactive demo, checkdemofolder. ...
This is pytorch implementation of paper "Glow: Generative Flow with Invertible 1x1 Convolutions". Most modules are adapted from the offical TensorFlow versionopenai/glow. TODO Glow model. The model is coded as described in original paper, some functions are adapted from offical TF version. Most ...
Last commit message Last commit date Latest commit History 33 Commits celeba_z glow hparams pictures vision .gitignore LICENSE infer_celeba.py readme.md test_modules.py train.py README MIT license Glow This is pytorch implementation of paper "Glow: Generative Flow with Invertible 1x1 Convolutions...
Glow: Generative flow with invertible 1x1 convolutions. In NeurIPS, 2018. 1, 2, 4, 6, 7 [25] Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling. Improved variational in- ference with inverse autoregressive flow. In NeurIPS, 2016. 2 [26] Diederik...
Code for reproducing results in"Glow: Generative Flow with Invertible 1x1 Convolutions" To use pretrained CelebA-HQ model, make your own manipulation vectors and run our interactive demo, checkdemofolder. Requirements Tensorflow (tested with v1.8.0) ...
GoogleNet-Going deeper with convolutions 深入研究卷积 摘要 我们提出了一个名为“inception”的深度卷积神经网络,目标是将分类、识别ILSVRC14数据集的技术水平提高一个层次。这一结构的主要特征是对网络内部计算资源利用进行优化。这一目标的实现是通过细致的设计,使得在保持计算消耗不变的同时增加网络的宽与深。为了使...
"Glow: Generative Flow with Invertible 1x1 Convolutions." Advances in Neural Information Processing Systems 31 (2018): 10215-10224. https://arxiv.org/abs/1807.03039 ^Dinh, Laurent, Jascha Sohl-Dickstein, and Samy Bengio. "Density estimation using real nvp." arXiv preprint arXiv:1605.08803 (...