[15] Gidaris, Spyros et al. “Boosting Few-Shot Visual Learning with Self-Supervision.” ICCV 2019 [16] Zhai, Xiaohua et al. “SL: Self-Supervised Semi-Supervised Learning.”” ICCV 2019 [17] Sermanet, Pierre et al. “Time-Contr...
[16] Zhai, Xiaohua et al. “SL: Self-Supervised Semi-Supervised Learning.”” ICCV 2019 [17] Sermanet, Pierre et al. “Time-Contrastive Networks: Self-Supervised Learning from Video.” 2018 IEEE International Conference on Robotics and Automation (ICRA) (2017): 1134-1141. [18] Wang, ...
半监督习(Semi-supervised):半监督介于监督学习和无监督之间,即训练集中只有一部分数据有标签,需要通过伪标签生成等方式完成模型训练; 弱监督(Weakly-supervised):弱监督是指训练数据只有不确切或者不完全的标签信息,比如在目标检测任务中,训练数据只有分类的类别标签,没有包含Bounding box坐标信息。 在上述概念中,无监督...
[16] Zhai, Xiaohua et al. “SL: Self-Supervised Semi-Supervised Learning.”” ICCV 2019 [17] Sermanet, Pierre et al. “Time-Contrastive Networks: Self-Supervised Learning from Video.” 2018 IEEE International Conference on Robotics and Automation (ICRA) (2017): 1134-1141. [18] Wang, Xiaol...
半监督(semi-supervised learning):利用好大量无标注数据和少量有标注数据进行监督学习; 远程监督(distant-supervised learning):利用知识库对未标注数据进行标注; 无监督:不依赖任何标签值,通过对数据内在特征的挖掘,找到样本间的关系,比如聚类相关的任务。
CVPR19和ICCV19上,Google Brain的几个研究员发表了两篇论文,从另外的视角分析和研究self-supervised learning问题。两篇paper名字分别是:Revisiting Self-Supervised Visual Representation Learning(CVPR19)和S^4L: Self-Supervised Semi-Supervised Learning(ICCV19) 。
vime-semi-supervised 这篇paper比较有意思的地方在于同时引入了自监督和半监督的方法 on tabular domain,前言提高的许多技术也具有很好的参考性。 cv和nlp上的pretrain task为什么不能生搬硬套到tabular上? 因为它们严重依赖图像或语言数据的空间或语义结构。 对于语言领域,经典的lm task,基于前n个token预测下一个tok...
One method for dealing with this is semi-supervised learning which is commonly split into a self-supervised pretext task and a subsequent model fine-tuning. Here, we compress this two-stage training into one by introducing S5CL, a unified framework for fully-supervised, self-supervised, and ...
此外，CVPR19和ICCV19上，Google Brain的研究员从不同角度深入研究自监督学习问题，分别发表的《Revisiting Self-Supervised Visual Representation Learning》和《S^4L: Self-Supervised Semi-Supervised Learning》论文，对自监督学习的网络结构、任务组合进行了详尽分析。其中，S^4L论文采用多任务学习策略，...
CVPR19和ICCV19上,Google Brain的几个研究员发表了两篇论文,从另外的视角分析和研究self-supervised learning问题。两篇paper名字分别是:Revisiting Self-Supervised Visual Representation Learning(CVPR19)和S^4L: Self-Supervised Semi-Supervised Learning(ICCV19) 。