GitHub - CuriousAI/mean-teacher: A state-of-the-art semi-supervised method for image recognitiongithub.com/CuriousAI/mean-teacher 由于原文的代码是使用比较久远的PyTorch版本,存在一些因版本冲突导致的bug,所以这里给出fork源码并做修改的代码版本: https://github.com/Hugo-cell111/mean-teachergithub...
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...
在ramp-up阶段,α的值设定为0.99,之后的训练过程中设置为0.999(\alpha越大,student model的参数对teacher model的参数影响越小)。这是因为初始时 student 模型训练的很快,而 teacher 需要忘记之前的、不正确的 student 权重;在 student 提升很慢的时候, teacher 记忆越长越好。 此段内容参考:半监督学习:Π-Model...
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...
To overcome this problem, we propose Mean Teacher, a method that averages model weights instead of label predictions. As an additional benefit, Mean Teacher improves test accuracy and enables training with fewer labels than Temporal Ensembling. Without changing the network architecture, Mean Teacher ...
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 ...
作者提出Mean Teacher方法来解决上述问题。Mean Teacher方法对模型的权重而不是预测标签进行平均,获得了准确性的提升。 Introduction 深度学习模型需要大量的参数去学习有用的特征抽象,这使得他们容易产生过拟合。然而,手工添加高质量标签的成本是非常高的。因此,需要在半监督学习中使用正则化方法有效利用未标记的数据去减小...
My teacher is supposed to go to with her boss. At the same time, Michael has with another client. Michael has had ever to go to the meeting. That we thinks that he can't go. Michael tells david that he had enough time to go to the meeting with mr. Wilson because. Pair work. ...
adj. mean·er, mean·est 1. a. Lacking in kindness; unkind: The teacher was not being mean in asking you to be quiet. b. Cruel, spiteful, or malicious: a mean boy who liked to make fun of others. c. Expressing spite or malice: gave me a mean look. d. Tending toward or ch...
为了克服这个问题,我们提出Mean Teacher,一种平均模型权重而不是标签预测的方法。Mean Teacher在训练时使用比时序集成更少的标签,还能提高测试的准确性。在不改变网络结构的情况下,Mean Teacher在250个标签的SVHN上的错误率为4.35%,优于1000个标签训练的Temporal ensemble。我们还证明了良好的网络架构对性能至关重要。