Total-variation regularization 除了上面两种损失以外,还可以加入一种正则化手段:全变差正则化(Total-variation regularization),证明能够增加图像的平滑度,具体计算就是图片的相邻的行像素相减,相邻的列像素相减,如图所示(列相减的情况类似): 具体公式就是: 这可以不用循环写: def tv_loss(img, tv_weight): """ ...
Total-variation regularization 除了上面两种损失以外,还可以加入一种正则化手段:全变差正则化(Total-variation regularization),证明能够增加图像的平滑度,具体计算就是图片的相邻的行像素相减,相邻的列像素相减,如图所示(列相减的情况类似): Total-variation regularization示意图 具体公式就是: 这可以不用循环写: deftv...
2.在训练期间输入中级训练结果。 3.添加论文中所描述的总变差正则化(Total Variation Regularization)。 使用官方预训练的VGG模型 论文《基于感知损失的实时风格迁移和超分辨率》中的模型架构 首先,我们需要快速浏览一下该模型架构。可以说该论文的主要贡献在于,它提出将生成的图像反馈到预先训练的图像分类模型,并从一些...
- style_weights: list of weights to use for each layer in style_layers - tv_weight: weight of total variation regularization term - init_random: initialize the starting image to uniform random noise """ # Extract features for the content image content_img = preprocess(PIL.Image.open(content...
-tv_weight: Weight of total-variation (TV) regularization; this helps to smooth the image. Default is 1e-3. Set to 0 to disable TV regularization. -num_iterations: Default is 1000. -init: Method for generating the generated image; one of random or image. Default is random which uses ...
1000- SDPoint: Implementation of "Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks", published in CVPR 2018. 1000- SRDenseNet-pytorch: 极深网络,SRDenseNet-pytorch,论文:基于密集跳跃连接的图像超分辨率(ICCV_2017)。 1000- GAN_stability: Code for...
Seg-Uncertainty: Unsupervised Scene Adaptation with Memory Regularization in vivo, In IJCAI 2020. Talks & conferences PyTorch Conference 2018: First PyTorch developer conference at 2018. Pytorch elsewhere 空文件 简介 A comprehensive list of pytorch related content on github,such as different models,imple...
1000- SDPoint: Implementation of "Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks", published in CVPR 2018. 1000- SRDenseNet-pytorch: 极深网络,SRDenseNet-pytorch,论文:基于密集跳跃连接的图像超分辨率(ICCV_2017)。 1000- GAN_stability: Code for...
Neural Machine Translation is the practice of using Deep Learning to generate an accurate translation of text from one language to another.
StyleTransfer-PyTorch风格迁移Content lossStyle lossTotal-variation regularization结果链接 在上次的Network Visualization之后,这次接着是Style Transfer,选择的还是PyTorch版本,个人认为这次代码还是比较简单有趣,也是直接在StyleTransfer-PyTorch.ipynb编写完成的