【文章推荐】论文笔记（6）：Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation

原文：论文笔记（6）：Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation

这篇文章的主要贡献点在于： .实验证明仅仅利用图像整体的弱标签很难训练出很好的分割模型 .可以利用bounding box来进行训练，并且得到了较好的结果，这样可以代替用pixel level训练中的ground truth .当我们用少量的pixel level annotations和大量的图像整体的弱标签来进行半监督学习时，其训练效果可和全部使用pixel level annotations ...

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论文笔记之：Semi-supervised Classification with Graph Convolutional Networks

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SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS 论文笔记

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论文笔记之：Semi-Supervised Learning with Generative Adversarial Networks

Semi-Supervised Learning with Generative Adversarial Networks 　　　　引言：本文将产生式对抗网络（GAN）拓展到半监督学习，通过强制判别器来输出类别标签。我们在一个数据集上训练一个产生式模型 G 以及一个判别器 D，输入 ...

论文笔记（3）：STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation

论文题目是STC，即Simple to Complex的一个框架，使用弱标签（image label）来解决密集估计（语义分割）问题。 2014年末以来，半监督的语义分割层出不穷，究其原因还是因为pixel级别的GroundTruth太难标注，因此弱监督成了人们研究的一个热门方向。作者的核心 ...

[CVPR2015] Is object localization for free? – Weakly-supervised learning with convolutional neural networks论文笔记

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论文笔记：Adaptive Consistency Regularization for Semi-Supervised Transfer Learning (CVPR 2021)

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[CVPR2017] Deep Self-Taught Learning for Weakly Supervised Object Localization 论文笔记

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[论文][半监督语义分割]Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision

CVPR2021 原文半监督语义分割方法的总结：主要思想： Consistency regularization ：希望不同扰动之下网络的输出结果一致，扰动的加入的位置：（1）在输入图片上加扰 ...

原文：论文笔记（6）：Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation

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