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View Code? Open in Web Editor NEWEnAET: Self-Trained Ensemble AutoEncoding Transformations for Semi-Supervised Learning
Home Page: https://arxiv.org/abs/1911.09265
License: MIT License
EnAET: Self-Trained Ensemble AutoEncoding Transformations for Semi-Supervised Learning
Home Page: https://arxiv.org/abs/1911.09265
License: MIT License
您好,我在复现您另一篇文章AdCo时发现训练不收敛。目前的操作是:
1、组建batch时,每个id选两张图片;
2、同一batch分两次前向输入backbone,获得特征;
3、batch特征和neg feats算负样本间相似度,同一个id内的两张样本算正样本相似度,然后concat在一起,label为0,用logit softmax算loss;
4、backbone的loss用对应的t计算,且只更新backbone参数,neg feats只更新neg feats,因为neg feat是maxmize,所以loss乘以了-1;
5、上述optimizer是交替优化。
还请帮忙看看我的理解是不是对的哈。
精彩的工作!请问,与S4L: Self-Supervised Semi-Supervised Learning这篇文章有什么异同点
Hello,
I was wondering in the train.log what loss, Closs, Eloss, wd, waet and top1avg mean?
Thanks for any clarification!
Warm regards,
Andrew
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