比如，用于预训练模型的自监督学习也在很多论文中被说成是“无监督表征学习”（Unsupervised Representation...
在深度学习领域，自监督(self-supervised)与无监督(unsupervised)的关系在定义上存在一定的模糊性，关键取决于对有监督学习的界定。传统意义上，有监督学习依赖于人为标注的指导信息，而自监督学习则通过在数据内部构造的监督信号进行学习，无需人工标注。具体而言，自监督学习可以通过多种方式构建监督信号，例...
有监督(Supervised):监督学习是从给定的带标签训练数据集中学习出一个函数(模型参数),在输入新的测试数据时,可以根据这个函数预测结果; 无监督(Unsupervisedg):无监督学习是从无标签数据中分析数据本身的规律性等解析特征。无监督学习算法分为两大类:基于概率密度函数估计的方法和基于样本间相似性度量的方法; 半监督习...
self-supervised是unsupervised的一种。然而unsupervised learning尝试着学习数据的模式,像聚类,异常检测等,他是完全没有标签的。而self-supervised learning关注点在于恢复,他有标签,但是标签往往是自己预测的,比如我拿出一张照片anchor,将其做翻转得到x,那么这两个照片标签一样,再随机抽取一张照片y,他和anchor的标签视...
source: [Momentum Contrast for Unsupervised Visual Representation Learning](https://arxiv.org/abs/1911.05722) MoCo 通过工程的方式,和一些 trick,比如 model ema 和 shuffleBN 来解决之前没法很好 sample 负样本的问题。 SimCLR 最近,hinton 组也...
这里主要关注Unsupervised learning一类特定的方法:Self-supervised learning(自监督学习)。自监督学习的思想非常简单,就是输入的是一堆无监督的数据,但是通过数据本身的结构或者特性,人为构造标签(pretext)出来。有了标签之后,就可以类似监督学习一样进行训练。
Though self-supervised learning is a technically a subset ofunsupervised learning(as it doesn’t require labeled datasets), it’s closely related tosupervised learningin that it optimizes performance against a ground truth. This imperfect fit with both conventional machine learning paradigms led to th...
A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning 何恺明+Ross Girshick:深入探究无监督时空表征学习 3. Zero-Shot 最近因为CLIP的出现,Zero-Shot可能会引起一波热潮,ViLD将CLIP成功应用于目标检测领域,相信未来会有越来越多的基于CLIP的Zero-Shot方法。
For this reason, this paper proposes unsupervised new-set domain adaptation with self-supervised knowledge (SUNDA) to transfer the sample contrastive knowledge from the source domain, and use self-supervised knowledge from the target domain to guide the knowledge transfer. ...
We propose a self-supervised representation learning model for the task of unsupervised phoneme boundary detection. The model is a convolutional neural network that operates directly on the raw waveform. It is optimized to identify spectral changes in the signal using the Noise-Contrastive Estimation ...