在下一次迭代中,student被视为teacher,并重复相同的过程直到达到预定的迭代次数或model的size在可接受范围内达到最大。 Self-supervised Semi-supervised Learning 自我监督半监督学习(S4L)[229]通过使用自我监督学习[230]技术从图像数据库中学习有用的表示来解决SSL问题。S4L的架构如图6(5)所示。引人注目的自我监督...
def training_step(self, batch, idx): x, y = batch mask = self.get_mask(x) #和ssl中一样,先获得mask矩阵,概率由前面的超参数确定 # This is now 3D tensor of augmented original data augments = torch.stack( [self.get_pretext(x, mask)[0] for _ in range(self.n_augments)], dim=1 ...
Self-supervised learning is a promising new technique for learning representative features in the absence of manual annotations. It is particularly efficient in cases where labeling the training data is expensive and tedious, naturally linking it to the semi-supervised learn- ing paradigm. In this ...
While supervised (and semi-supervised) learning requires an external “ground truth,” in the form of labeled data, self-supervised learning tasks derive the ground truth from the underlying structure of unlabeled samples. Many self-supervised tasks are not useful unto themselves: their utility lies...
论文:Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification GitHub:https://github.com/emadeldeen24/CA-TCC TPAMI期刊论文,是CCF-A类的期刊论文,IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)。
Self-supervised learning is a promising new technique for learning representative features in the absence of manual annotations. It is particularly efficient in cases where labeling the training data is expensive and tedious, naturally linking it to the semi-supervised learning paradigm. In this work,...
(3)Self-training结果优化:如果神经网络的输出是一个分布,我们希望这个分布要集中 4.Smoothness Assumption(第二个假设) (1)核心思想:假设特征的部分是不均匀的(在某些地方集中,某些地方分散),如果两个特征在高密度区域是相近的,那么二者的标签是相同的。
半监督学习(Semi-supervised Learning)的魔法 一、半监督学习的三个常见的基本假设 1. 连续性假设(Smoothness Assumption) 2. 聚类假设(Cluster Assumption) 3. 流形假设(Manifold Assumption) 二、常见的半监督 机器学习方法 1. 自训练(Self-training)
self-training算法: 还是两个样本集合:Labled={(xi,yi)};Unlabled= {xj}. 执行如下算法 Repeat: 1. 用L生成分类策略F; 2. 用F分类U,计算误差 3. 选取U的子集u,即误差小的,加入标记.L=L+u; 重复上述步骤,直到U为空集. 上面的算法中,L通过不断在U中,选择表现良好的样本加入,并且不断更新子集的算...
paper: Big Self-Supervised Models are Strong Semi-Supervised Learners 作者:Ting Chen, Geoffrey Hinton 时间:2020 摘要 在充分利用大量未标记数据的同时,从少数标记示例中学习的一个范例是无监督的预训练,然后是有监督的微调。我们的方法的一个关键要素是在预训练和微调期间使用大(深度和广度)的网络。 我们发现...