This paper proposes a broader view: that autoencoders are generic circuits for learning invariant features. (自编码器是学习不变特征的通用电路。) Instead of reconstructing images from noisy versions, MTAE learns to transform the original image into analogs in multiple related domains.(MTAE 不会从...
想一下从一张照片识别猫狗是不是representation and reasoning system? 是的,CNN的卷积层做的就是representation learning,全连接层做的是reasoning。 representation除了用Deep learning可以自动学到,还可以通过feature engineering手工获得。 5. 那么如何进行representation? 首先这个representation要满足一定条件: 做个题吧:...
一、DeepNLP的核心关键:语言表示(Representation) 最近有一个新名词:Deep Learning + NLP = DeepNLP。当常规的机器学习Machine Learning升级发展到了一定的阶段后,慢慢的被后起的深度学习Deep Learning夺势而去,并如火如荼地引领了一波新高潮,因为Deep Learning有machinelearning过而不及之处!那当Deep Learning进入自...
且训练的损失函数是多个异质的损失函数的加权和，这类深度学习任务一般被称为表征学习。
Learning deep face representation with long-tail data: An aggregate-and-disperse approach - ScienceDirect In this work, we study the problem of deep representation learning on a large face dataset with long-tail distribution. Training convolutional neural netwo... YMA B,MKA B,SSAB C,... - ...
题目:Deep Learning Face Representation from Predicting 10,000 Classes 主要内容:通过深度学习来进行图像高级特征表示(DeepID),进而进行人脸的分类。 长处:在人脸验证上面做,能够非常好的扩展到其它的应用,而且夸数据库有效性;在数据库中的类别越多时,其泛化能力越强,特征比較少,不像其它特征好几K甚至上M,好的泛...
Tu, "Deeply-supervised nets", in Deep Learning and Representation Learning Workshop, NIPS, 2014.Lee, Chen-Yu, Xie, Saining, Gallagher, Patrick, Zhang... CY Lee,S Xie,P Gallagher,... - 《Eprint Arxiv》 被引量: 939发表: 2014年 cuDNN: Efficient Primitives for Deep Learning Catanzaro,...
题目:Deep Learning Face Representation from Predicting 10,000 Classes 主要内容:通过深度学习来进行图像高级特征表示(DeepID),进而进行人脸的分类。 优点:在人脸验证上面做,可以很好的扩展到其他的应用,并且夸数据库有效性;在数据库中的类别越多时,其泛化能力越强,特征比较少,不像其他特征好几K甚至上M,好的泛化...
We show that the margins can be easily deployed in standard deep learning framework through quintuplet instance sampling and the associated triple-header hinge loss. The representation learned by our approach, when combined with a simple k-nearest neighbor (kNN) algorithm, shows significant ...
Unlike the previous methods that consider simple low-level features such as gray matter tissue volumes from MRI, mean signal intensities from PET, in this paper, we propose a deep learning-based feature representation with a stacked auto-encoder. We believe that there exist latent complicated ...