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Add a description, image, and links to the autoencoder topic page so that developers can more easily learn about it. Curate this topic Add this topic to your repo To associate your repository with the autoencoder topic, visit your repo's landing page and select "manage topics." Learn...
Data augmentationMaximum mean discrepancy (MMD)In this paper, we propose a novel, effective and simpler end-to-end image clustering auto-encoder algorithm: ICAE. The algorithm uses predefined evenly-distributed class centroids (PEDCC) as the clustering centers, which ensures the inter-class distance...
In this paper, we propose a multi-view clustering algorithm based on multiple auto-encoder, named MVC-MAE (see Fig.1). Specially, MVC-MAE first employs multiple auto-encoders to capture the nonlinear structure information in multi-view data and derive the low-dimensional representations of data...
Autoencoder based methods generalize better and are less prone to overfitting for a data restricted problem like ours, as the number of parameters that are to be learned/estimated is much smaller than the number of learnable parameters in matrix factorization or nuclear norm minimization (more on ...
【李宏毅2020 ML/DL】P59 Unsupervised Learning - Auto-encoder 我已经有两年 ML 经历,这系列课主要用来查缺补漏,会记录一些细节的、自己不知道的东西。 已经有人记了笔记(很用心,强烈推荐):https://github.com/Sakura-gh/ML-notes 本节对应笔记: https://github.com/Sakura-gh/ML-notes/blob/master/ML-...
We present a novel approach for analyzing financial time series data using a Long Short-Term Memory Autoencoder (LSTMAE), a deep learning method. Our primary objective is to uncover intricate relationships among different stock indices, leading to the extraction of stock networks. We examine time...
已经有人记了笔记(很用心,强烈推荐):https://github.com/Sakura-gh/ML-notes 本节对应笔记: https://github.com/Sakura-gh/ML-notes/blob/master/ML-notes-md/22_Unsupervised%20Learning%2...李宏毅-DeepLearning-2017-Unsupervised Learning:Deep Auto-encoder Auto-encoder使用神经网络进行降维。由于这里是无...
An autoencoder layer is a component of a neural network that encodes input data into a lower-dimensional representation and then decodes it to reconstruct the original input, identifying hidden semantic features in the process. AI generated definition based on: Computer Science Review, 2022 About ...
Learning an expressive conditional likelihood distribution enables the latent code to only capture the abstract and high-level information of the data, while the remaining low-level information is captured by the implicit conditional likelihood distribution. We show the applications of implicit autoencoder...