Multi-class-Peer-Loss-functions:通过采用对等预测损失功能来学习带有噪声的标签（深度学习和多类版本）-源码
对等丢失功能
此存储库是ICML2020接受的“”的Pytorch Pytorch实现。
所需的包装和环境
支持的操作系统： Windows，Linux，Mac OS X； 的Python：3.6 / 3.7;
深度学习库： PyTorch（需要GPU）
所需软件包： Numpy，Pandas，random，sklearn，tqdm，csv，火炬（如果要估计噪声转换矩阵，则需要Keras）。
实用工具
该存储库包括：
:clipboard: 对等丢失功能的多类实现；
:clipboard: 深度学习中的对等丢失功能；
:clipboard: Peer Loss功能的动态调整策略可进一步提高性能。
运行详细信息（ 每个文件夹的README.md文件中都提到了MNIST，Fashion MNIST，CIFAR-10，CIFAR-100上具有不同噪声设置的对等丢失功能。
的工作流程 加权对等损失函数包括：
决策边界可视化
给定2D合成
文件下载
资源详情
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