将具有最大预测概率的类作为伪标签。形式化后等价于熵正则化(Entropy Regularization)或熵最小化(Entropy Minimization). 根据半监督学习的假设,决策边界应该尽可能通过数据较为稀疏的区域,即低密度区域,从而避免把密集的样本数据点分到决策边界的两侧,也就是说模型需要对未标记数据做出低熵预测,即熵最小化。伪标签方...
伪标签方法在半监督学习中，通过利用未标记数据与标记数据共同训练，提高模型泛化性能。其核心思想是将具有最大预测概率的类作为伪标签。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...
网络熵正则化方法 网络释义 1. 熵正则化方法 向量熵正则... ... ) entropic regularization 熵正则化 )entropy regularization熵正则化方法) vector optimization 向量优化 ... www.dictall.com|基于2个网页
1) entropy regularization 熵正则化方法 1. For the generalized minmax problem,we establish a rigorous duality relationship betweenentropy regularizationand exponential penalty methods,with an attempt to a bridge between entropy optimization and conventional optimization methods. ...
{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\...
Entropy regularizationLinear-quadratic gamesEntropy regularization has been extensively adopted to improve the efficiency, the stability, and the convergence of algorithms in reinforcement learning. Thisdoi:10.2139/ssrn.3702956Guo, XinXu, RenyuanZariphopoulou, Thaleia...
In this chapter, we motivate the use of entropy regularization as a means to benefit from unlabeled data in the framework of maximum a posteriori estimation. The learning criterion is derived from clearly stated assumptions and can be applied to any smoothly parametrized model of posterior ...
3) maximum entropy regularization method 最大熵正则化方法4) improved entropy method 改进的熵值法 1. The indicators of environmental pressure of industry were selected from the view of resource and energy consumption and environmental pollution firstly,and improved entropy method was introduced to ...
ICASSP 2023:ENTROPY BASED FEATURE REGULARIZATION TO IMPROVE TRANSFERABILITY OF DEEP LEARNING MODELS 分类任务中的标签往往只包含了数据集中的部分内容信息,例如自然图像中包含多个对象,但是标签中只有一个对象被标记。在使用 crossentropy 在这样的“粗标签”上进行训练时,对导致模型在“粗标签”上的过拟合,从而丢失...
2.3 Entropy Regularization We drop the reference to parameter θ in fk and gk to lighten the notation. The MAP estimate is the maximizer of the posterior distribution, the maximizer of \begin{align} C(\theta,\lambda;\mathcal{L}_{n})&=L(\theta;\mathcal{L}_{n})-\lambda H_{\text{...