低密度分离假设就是假设数据非黑即白,在两个类别的数据之间存在着较为明显的鸿沟,即在两个类别之间的边界处数据的密度很低(即数据量很好)。 3.1 自训练(Self-training) 自训练的方法十分直觉,它首先根据有标签的数据训练出一个模型,将没有标签的数据作为测试数据输进去,得到没有标签的数据的一个为标签,之后将一...
temporal ensembling相对于Π-model的好处: 1,训练更快,因为一个epochs不再用计算两个output z; 2,训练结果比Π-model更不noisy(具体啥意思作者没说,应该就是结果更稳定吧)。 Second, the training targets\tilde zcan be expected to be less noisy than with Π-model. 参考资料 https://blog.csdn.net/u0...
详见论文Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning Q: 还有什么想说的吗? A:从这一篇论文开始,开始试着回答ReadPaper给出的论文十问。会选择性地回答部分问题,也会将一些问题留在代码分析上。这并不是教条式的论文阅读方式,而是通过拓展思考,发掘论文以外的...
Mean Teacher is a simple method for semi-supervised learning. It consists of the following steps: Take a supervised architecture and make a copy of it. Let's call the original model the student and the new one the teacher. At each training step, use the same minibatch as inputs to both...
Temporal Ensembling中采用的label平均的方法,可以在每一个training step更新teacher model,及时的指导student model的学习。在ImageNet 半监督学习 ),而 Temporal ensembling 的第二项是 无监督 loss,是面向全部数据的。 πmodel 的无监督代价是对同一个输入在不同的正则和数据增强条件下的一致性。即要求在不同...
3.1.1 Training S 分割网络S使用损失LS进行训练,损失LS是三种损失的组合:标准交叉熵损失、特征匹配损失和自训练损失。 交叉熵损失。对有监督的数据的损失。这是一个标准的监督像素交叉熵损失项Lce。 Feature matching loss 为了使得分割结果 和标签 的特征分布尽可能一致,本文计算分割结果 ...
Without sufficient high-quality annotations, the\nusual data-driven learning-based approaches struggle with deficient training.\nOn the other hand, directly introducing additional data with low-quality\nannotations may confuse the network, leading to undesirable performance\ndegradation. To address this ...
While θs are updated with an optimizer of choice, the teacher’s weights θjt at training iteration j are updated as follows: (1)θjt=βθj−1t+(1−β)θjs,where β controls the temporal length of the ensemble. As the name suggests, the teacher is present in ECMT but it does ...
Mean Teacher is a simple method for semi-supervised learning. It consists of the following steps: Take a supervised architecture and make a copy of it. Let's call the original model thestudentand the new one theteacher. At each training step, use the same minibatch as inputs to both the...
label prediction ensemble: 2016 Temporal Ensembling for Semi-Supervised Learning (EMA prediction for each training sample) Π-model: 2016 Temporal Ensembling for Semi-Supervised Mean-Teacher Method 令θt 表示 t 时刻的student model parameter, θt′ 表示 t 时刻的...