Comments (3)
- 并没有胡乱比较。我找的时候,都是基于full-training的,因为这块用的最多
- 大多数结果都有出处,真用的时候,肯定是要看出处的
from transferlearning.
https://github.com/jindongwang/transferlearning/blob/master/doc/benchmark.md 这个里面decaf出处这个文章是有问题的,里面一些对比方法好多是基于sampling source的,还是不要乱传播这些有问题的实验结果比较好。
from transferlearning.
这个文章有没有问题我不知道,我说过了,里面都有出处,真要用的时候还是需要看看。不明白你到底觉得哪里不好。。
你如果有时间的话,可否详细指出是哪个方法的结果不对,然后更正?
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Related Issues (20)
- Deep DG HOT 9
- MMD距离 backward问题 HOT 3
- BDA代码中的数据集的问题 HOT 2
- 关于TransferNet中source_clf计算的疑问 HOT 1
- 关于复现 HOT 1
- 对比算法的复现缺少部分流程 HOT 1
- 程序显示没有loss_funcs模块 HOT 1
- Our new test-time adaptation algorithm for segmentation HOT 3
- 关于transferlearning/code /DeepDA的模型代码读取 HOT 2
- How to use DIFEX for single domain generalization? HOT 2
- code add HOT 2
- 有关于在使用DG中DANN方法中遇到的问题
- Office-31 webcam域上微调模型丢失 HOT 1
- 是否需要进行微调? HOT 2
- 作者您好!请问AdaRNN的对比实验中,MMD-RNN和DANN-RNN是如何实现的,MMD-RNN的源域和目标域是如何定义的,AdaRNN相比这两个对比模型进步在哪里? HOT 1
- Feature Request:添加ADDA的代码以及与其他方法的比较。 HOT 1
- 王老师您好,Domain Generalization for Activity Recognition via Adaptive Feature Fusion请问这篇论文的代码具体在哪个文件夹里?找了很久没找到 HOT 1
- Time series domain adaptation benchmark and datasets HOT 1
- DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization中的cross_dataset的文件 HOT 1
- BDA中A-distance问题
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