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...
伪标签方法是一种同时从未标记数据和标记数据中学习的监督范式。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...
Furthermore, a new self-entropy loss function is proposed, which can pay more attention to the hard samples and reduce the uncertainty of the network prediction. Experimental results show that our method achieved an average Dice of 89.32% and an average IOU of 81.42% in segmentation of MH ...
minimizationBiopolymersWe introduce the principle of sequential minimization of entropy loss (SMEL) in the context of biopolymer folding in vitro. This principle asserts that at each stage in the dominant folding pathway, the conformational entropy loss, denoted ΔSloop, associated with loop closure ...
Ghasedi Dizaji K, Herandi A, Deng C, et al. Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization[C]//Proceedings of the IEEE international conference on computer vision. 2017: 5736-5745. 摘要翻译 ...
pytorch的Entropy Minimization (EM) 的实现,#p_logit:[batch,class_num]defentropy_loss(p_logit):p=F.softmax(p_logit,dim=-1)return-
two proposed approaches for entropy minimization using (i) an unsupervised entropy loss (ii) adversarial training. 1 动机 基于熵的UDA方法动机 根据图1中的熵图可以看出,原域图片的熵值很小,目标域图片的熵值很大,熵值小意味着模型的预测结果是over-confident,熵值大意味着模型的预测结果是under-confident。作者...
Entropy generation is a parameter that quantifies the loss of exergy. Circular fins are relatively good heat transfer augmentation features with superior aerodynamic performance and as a result find application in some solar air heaters. In this paper, the entropy generation in a circular porous ...
"What will happen if we subtract entropy from loss (instead of adding it), to encourage "entropy maximization" not minimization, in order to encourage uniform distribution?" I want to close this issue after we all agree on something very clearly :) ...
The soft interpretation of the constraint S(Y|X)(f)=0 leads to the minimization of (2.3.101)Dμ(X,Y)(f):=(1−μ)S(Y|X)(f)−μS(Y)(f). Hence a collection of unsupervised data can be clustered into n groups by minimizing Dμ(X,Y)(f), which is in fact a soft-...