SSL的主要方法有consistency regularization和pseudo label(entropy regularization)。 需要注意的是,consistency regularization需要数据增强,主要通过模型在对输入微小扰动下输出不变性来提高模型性能,所以两种方法是正交的,可以放在一起使用。 简单来说的话, 对于图像任务的话,数据增强的代价较小,增强后的数据可靠性更高,那...
这等同于熵最小化（Entropy Minimization）或熵正则化（Entropy Regularization），即通过减少未标记数据的预测不确定性，使决策边界更适应数据分布，从而减少类重叠，提高类边界清晰度。Loss函数结合真实标签与伪标签，通过调节权重a(t)，优化过程中避免较差局部极小值，确保伪标签与真实标签一致性。伪标签方...
X, y, reg):7"""8用循环实现softmax损失函数9D,C,N分别表示数据维度,标签种类个数和数据批大小10Inputs:11- W (D, C):weights.12- X (N, D):data.13- y (N,): labels14- reg: (float) regularization strength1516Returns :17- loss18- gradient19"""2021loss = 0.022dW =np...
X, y, reg):7"""8用循环实现softmax损失函数9D,C,N分别表示数据维度,标签种类个数和数据批大小10Inputs:11- W (D, C):weights.12- X (N, D):data.13- y (N,): labels14- reg: (float) regularization strength1516Returns :17- loss18- gradient19"""2021loss = 0.022dW =np...
2016 年 Label Smoothing Regularization 被提出,如今引用量已经一万五多了,文章信息: Rethinking the Inception Architecture for Computer Vision https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/44903.pdf 目标是提出一种正则项来缓解这种噪声带来的过拟合影响,理解起来很简单,...
The aim of this paper is to provide new theoretical and computational understanding on two loss regularizations employed in deep learning, known as local e... Nicolas García Trillos,Zachary Kaplan,D Sanz-Alonso - 《Entropy》 被引量: 0发表: 2019年 Regularization of currents and entropy Let T...
{s}\right)$ towards a few actions or action sequences, since it is easier for the actor and critic to overoptimise to a small portion of the environment. To reduce this problem, entropy regularization adds an entropy term to the loss to promote action diversity:$$H(X) = -\sum\pi\...
The procedure takes the form of entropy-regularization of the Bayesian estimate. While being computationally convenient with respect to alternative strategies, it is also theoretically justified as a correction to the Bayesian loss function used for point estimation and, as such, can be applied to ...
Output paths with peaky distribution.尖峰的存在不适合序列分割任务。 回到顶部 EnCTC 论文提出了基于最大熵的正则化方法EnCTC去抑制最大概率路径的存在: To remedy this, we propose a regularization method based on maximum conditional entropy which penalizes peaky distributions and encourages exploration. ...
the Cross-Entropy Method 文中的对比模型来自:Why You Should Use Cross-Entropy Error Instead Of Classification...用 classification error 大多数人望文生义的 loss,可能是这个公式。 classification error=count of error itemscount of all READING NOTE: Learning Spatial Regularization with Image-level Supervis...