An active learning method using consistency-based regularization according to an embodiment, performing data augmentation on data learned every cycle; selecting data to be labeled based on consistency-based normalization loss using the data on which the data augmentation has been performed; and learning...
CSD类似于半监督分类中的一致性正则化(consistency regularization, CR),CR在输入图片 x 上应用一些扰动得到 x′ ,并最小化预测结果 f(x) 和f(x′) 之间的差异,从而帮助模型对给定的带扰动的输入更鲁棒。但是,很难直接将CR应用到一张图片多个目标框的目标检测问题中,因为对图片进行不同的扰动后,可能会产生不...
论文链接:Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization 代码链接:https://github.com/HeimingX/semi_seg_proto NeurIPS 2022的工作。斜体是我的补充。 Introduction 动机:半监督语义分割任务的一个挑战是大的类内变化,即属于同一类的区域即使在相同的图像中也可能表现出非常不同...
{MutexMatch: Semi-Supervised Learning With Mutex-Based Consistency Regularization}, year={2024}, volume={35}, number={6}, pages={8441-8455}, keywords={Training;Predictive models;Semisupervised learning;Data models;Task analysis;Entropy;Labeling;Mutex-based consistency regularization;semi-supervised ...
To address this issue, a new SSL method based on the principle of consistency regularization is proposed in this study to achieve the bearing fault diagnosis task under the limited labeled samples situation. In the proposed method, a DA method (DAM) designed for one-dimensional bearing fault ...
3D MRI brain tumor segmentation using autoencoder regularization. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer, Cham; 2019 [cited 2023 Oct 24]. p. 311–20. https://doi.org/10.1007/978-3-030-11726-9_28 Schmidt AM, Desai AD, Watkins LE, Crowder HA...
By incorporating the approximated ACP and the data fidelity term Ψ(x|y, T ) = Y −X 2 2 (Y = T (y)), we can obtain the novel ACP-driven denoising algorithm: xˆ = arg min Ψ(x|y, T )+λJA⋆CP(x|T , K , Λ), x (6) where λ is the ...
MetaMix: Improved Meta-Learning with Interpolation-based Consistency Regularizationdoi:10.1109/ICPR48806.2021.9413158Training,Adaptation models,Training data,Speech recognition,Classification algorithms,Task analysisModel-Agnostic Meta-Learning (MAML) and its variants are popular few-shot classification methods. ...
作者提出了一个简单的用于目标检测的SSL框架——STAC,结合了基于伪标签的自训练方法(self-traing via pseudo label)和基于强数据增广的一致性正则化(consistency regularization)。该框架类似于Noisy-Student,包含多个阶段的训练:在第一阶段,使用所有标注数据训练目标检测器(即Teacher Model)直到收敛;随后在第二阶段,用...
Consistency regularizationData augmentationFew-shotFine-tuning pre-trained cross-lingual language models alleviates the need for annotated data in different languages, as it allows the models to transfer task-specific supervision between languages, especially from high- to low-resource languages. In this ...