概念定义和应用场景熵正则化和l1 l2 正则化类似,其使用形式都是在现有的loss function的基础上增加额外的一个正则损失项。 一图胜千言: 比较常见的使用方式是在模型的决策层的输出实施熵正则化,约束模型是输出…
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...
log_output=torch.log(soft_output)print('log_output:\n',log_output)#对比softmax与log的结合与nn.LogSoftmaxloss(负对数似然损失)的输出结果,发现两者是一致的。 logsoftmax_func=nn.LogSoftmax(dim=1)logsoftmax_output=logsoftmax_func(x_input)print('logsoftmax_output:\n',logsoftmax_output)#pyto...
In this paper, we propose a defense method that is formulated as an entropy regularization loss for model training, which can be augmented to the original training loss of super-resolution models. We show that various state-of-the-art super-resolution models trained with our defense method are...
Entropy regularization—a technique that introduces a penalty term based on policy entropy—ensures that agents retain sufficient randomness to explore alternative strategies without compromising on long-term performance. Stabilizing Learning with Entropy ...
{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\...
1.什么是伪标签 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…
(float) regularization strength1516Returns :17- loss18- gradient19"""2021loss = 0.022dW =np.zeros_like(W)2324num_classes = W.shape[1]25num_train =X.shape[0]2627foriinrange(num_train):28scores=np.dot(X[i],W)29shift_scores=scores-max(scores)30dom=np.log(np.sum(np.exp(shift_...
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. ...
为了帮助阐明这三种方法之间的差异,本文在图3的左子图中绘制了平均性能随m增加的变化,这表明UCE损失更鲁棒,对更大的margin不那么敏感,而原始marginal损失和Exclusive Regularization损失则不然。 图3 不同平衡策略的影响 然后,本文研究了balanced UCE损失的不同超参数。根据式(32),本文有两种替代方法来平衡UCE损失,即...