Feature Learning based Deep Supervised Hashing with Pairwise Labels

http://blog.csdn.net/abcjennifer/article/details/7804962 无监督学习近年来很热,先后应用于computer vision, audio classification和 NLP等问题,通过机器进行无监督学习feature得到的结果,其accuracy大多明显优于其他方法进行training.本文将主要针对Andrew的unsupervised learning,结合他的视频:unsupervised feature learning by A

来源:http://blog.csdn.net/abcjennifer/article/details/7804962 无监督学习近年来很热,先后应用于computer vision, audio classification和 NLP等问题,通过机器进行无监督学习feature得到的结果,其accuracy大多明显优于其他方法进行training.本文将主要针对Andrew的unsupervised learning,结合他的视频:unsupervised feature learning b

无监督特征学习——Unsupervised feature learning and deep learning 分类: Compression Computer Vision Machine Learning 杂感2012-07-31 15:48 36848人阅读 评论(61) 收藏 举报 目录(?)[+] 无监督学习近年来很热,先后应用于computer vision, audio classification和 NLP等问题,通过机器进行无监督学习feature得到的结果,其accurac

UFLDL Tutorial From Ufldl Jump to: navigation, search Description: This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning.  By working through it, you will also get to implement several feature learning/deep le

Background 1) "Patch-level image representation"的优势 "Patch-level image representation is very important for object classification and detection, since it is robust to spatial transformation, scale variation, and cluttered background" &

URL:http://ydwen.github.io/papers/WenECCV16.pdf这篇论文主要的贡献就是提出了Center Loss的损失函数,利用Softmax Loss和Center Loss联合来监督训练,在扩大类间差异的同时缩写类内差异,提升模型的鲁棒性. 为了直观的说明softmax loss的影响,作者在对LeNet做了简单修改,把最后一个隐藏层输出维度改为2,然后将特征在二维平面可视化,下面两张图分别是MNIDST的train集和test集,可以发现类间差异比较明显,但

Understanding, generalisation, and transfer learning in deep neural networks FEBRUARY 27, 2017 This is the first in a series of posts looking at the 'top 100 awesome deep learning papers.' Deviating from the normal one-paper-per-day format, I'll take

译自:http://sebastianruder.com/multi-task/ 1. 前言 在机器学习中,我们通常关心优化某一特定指标,不管这个指标是一个标准值,还是企业KPI.为了达到这个目标,我们训练单一模型或多个模型集合来完成指定得任务.然后,我们通过精细调参,来改进模型直至性能不再提升.尽管这样做可以针对一个任务得到一个可接受得性能,但是我们可能忽略了一些信息,这些信息有助于在我们关心的指标上做得更好.具体来说,这些信息就是相关任务的监督数据.通过在相关任务间共享表示信息,我们的模型在

UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS  ICLR 2016  摘要:近年来 CNN 在监督学习领域的巨大成功 和 无监督学习领域的无人问津形成了鲜明的对比,本文旨在链接上这两者之间的缺口.提出了一种 deep convolutional generative adversarial networks (DCGANs),that have certai