SwitchableNormalization-Keras:keras的可切换归一化方法-源码
可切换归一化-Keras
keras的可切换归一化方法
可切换归一化
本文的可归一化引入了可切换归一化（SN）。
博客解释SN方法。
SN适应权重3种不同的归一化方法，包括IN / LN / BN。 加权平均均值和方差分别需要3个可训练的平均权重和3个可训练的方差权重，因此SN的可训练参数等于2 * channels + 6 。
可切换规范化代码
实验
实验日志的详细信息位于目录实验中
比较3归一化方法： batch_norm（bn） // group_norm（gn） // switchable_norm（sn）
数据：ISBI 2D EM分割图像
网络：Unet（基于VGG）
纪元：5
环境：GeForce 1080Ti
结果：
训练时间：sn> gn> bn
细分结果：sn> gn> bn
文件下载
资源详情
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