迁移学习

迁移学习

Coral损失函数介绍

首先定义Coral损失函数

import torch
def CORAL(source, target, **kwargs):d = source.data.shape[1]ns, nt = source.data.shape[0], target.data.shape[0]# source covariancexm = an(source, 0, keepdim=True) - sourcexc = xm.t() @ xm / (ns - 1)# target covariancexmt = an(target, 0, keepdim=True) - targetxct = xmt.t() @ xmt / (nt - 1)# frobenius norm between source and targetloss = torch.mul((xc - xct), (xc - xct))loss = torch.sum(loss) / (4*d*d)return loss

用模拟数据进行验证

# 通过随机数模拟产生经过模型输出的结果source和target
# batch可以不一样，但分类类数要一样
source = torch.rand(64,4)  # 源域输出结果为batch=64， 4分类
target = torch.rand(64,4)  # 目标域域输出结果为batch=64， 4分类

Coral_loss = CORAL(source=source, target=target)
print(Coral_loss)

>>>output
tensor(3.3486e-05)

参考资料
链接: .