Comments (3)
您好~回复迟了不好意思。
- 我猜测更多是因为对抗训练本身在后期崩了导致的,这里可能你得改变一下损失或者调大R1正则化权重。
- 一定会有影响,因为同时训练,网络会权衡任务的难易从而自动调整在每种任务上所用的自身资源。
- 并不一定,因为我也发现模型在后期reference-guided任务会越来越差(也是因为HiSD原本并没有引入reference-guided的路线),但足够稳定的训练一般最后的模型不会比训练过程中最好的模型差,特别是适用了EMA做模型平均后。
- 这个没太理解。
from hisd.
好的好的,感谢您的详细回复!让我看待HiSD有了全新的视角,我再继续研究一下
from hisd.
不客气~ 有任何问题都可再交流
from hisd.
Related Issues (20)
- Virtual memory usage is too large HOT 4
- About Mutil-Gpus for training? HOT 7
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- 作者您好,想请问一下关于discriminator的问题 HOT 6
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- About beard model HOT 14
- A few questions about training tricks HOT 6
- 请教一下您的生成器和鉴别器模型是基于哪篇文章的 HOT 1
- 您好,请教一下论文中实验部分的一些问题,望指点 HOT 5
- 如何处理检查点文件? HOT 2
- 如何设置custom datasets? HOT 6
- 可以用m1芯片运行吗? HOT 3
- 更换数据集 HOT 2
- 关于生成图片模糊的问题 HOT 2
- Multi-tag task HOT 5
- 使用AFHQ数据集训练模型 HOT 13
- 求一个RaFD或者emotionnet数据集,想在celebA上做一下联合训练,有老哥有吗?我RaFD一直没申请到,emotionnet在网上下载的好像也是有问题的。
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from hisd.