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...
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...
Representation learningrefers to the process of learning a representationyi=f(xi)from an input objectxitoward a specific task, for example, classification, retrieval, clustering, and others. Recent advances indeep learning, that inherently incorporates representation learning following a hierarchy of repr...
This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations,...
representation learning,Knowledge representation learning和knowledge embeding learning可以理解成同一个概念,如果非要区分的 论文:Robotic Arm Representation Using Image-Based Feedback for Deep Reinforcement Learning robot is implemented in the V-Rep simulator. 本文提出了一种使用反馈图像通过深度强化学习(DRL)...
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...
神经网络(ConvolutionalNeuralNetworks, CNN)是一类包含卷积计算且具有深度结构的前馈神经网络(FeedforwardNeuralNetworks),是深度学习(deep learning)的代表算法之一。卷积神经网络具有表征学习(representationlearning)能力,能够按其阶层结构对输入信息进行平移不变分类(shift-invariant ...
在本文中,我们提出了一种新的深度学习跟踪器( deep learning tracker DLT),用于强大的视觉跟踪。我们尝试通过开发一种强大的判别跟踪器来结合生成和判别跟踪器背后的哲学,该跟踪器使用自动学习的有效图像表示。 DLT与其他现有跟踪器有一些关键特征。首先,它使用堆叠去噪自动编码器(stacked denoising autoencoder SDAE)来...
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...
The advancement of visual tracking has continuously been brought by deep learning models. Typically, supervised learning is employed to train these models with expensive labeled data. In order to reduce the workload of manual annotation and learn to track arbitrary objects, we propose an unsupervised...