Auto-Encoders in Deep Learning—A Review with New Perspectives
Shuangshuang Chen,Wei Guo +1 more
TL;DR: The auto-encoder is a key component of deep structure, which can be used to realize transfer learning and plays an important role in both unsupervised learning and non-linear feature extraction as mentioned in this paper .
read more
Abstract: Deep learning, which is a subfield of machine learning, has opened a new era for the development of neural networks. The auto-encoder is a key component of deep structure, which can be used to realize transfer learning and plays an important role in both unsupervised learning and non-linear feature extraction. By highlighting the contributions and challenges of recent research papers, this work aims to review state-of-the-art auto-encoder algorithms. Firstly, we introduce the basic auto-encoder as well as its basic concept and structure. Secondly, we present a comprehensive summarization of different variants of the auto-encoder. Thirdly, we analyze and study auto-encoders from three different perspectives. We also discuss the relationships between auto-encoders, shallow models and other deep learning models. The auto-encoder and its variants have successfully been applied in a wide range of fields, such as pattern recognition, computer vision, data generation, recommender systems, etc. Then, we focus on the available toolkits for auto-encoders. Finally, this paper summarizes the future trends and challenges in designing and training auto-encoders. We hope that this survey will provide a good reference when using and designing AE models.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Autoencoders and their applications in machine learning: a survey
Kamal Berahmand,Fatemeh Daneshfar,Elaheh Sadat Salehi,Yuefeng Li,Yue Xu +4 more
TL;DR: A comprehensive survey of autoencoders is presented, starting with an explanation of the principle of conventional autoencoder and their primary development process, and a taxonomy of autoencoders based on their structures and principles is provided.
116
A comprehensive review of synthetic data generation in smart farming by using variational autoencoder and generative adversarial network
Yaganteeswarudu Akkem,Saroj Biswas,Aruna Varanasi +2 more
TL;DR: This study reviews the application of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) for generating synthetic crop data, enhancing crop recommendation models with high-quality, realistic data, and improving their performance in agriculture.
49
Deep learning in computational mechanics: a review
Leon Herrmann,Stefan Kollmannsberger +1 more
TL;DR: This review focuses on deep learning methods rather than applications for computational mechanics, thereby enabling researchers to explore this field more effectively and help researchers identify key concepts and promising methodologies within this field.
42
Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods
Irena Galić,Marija Habijan,Hrvoje Leventic,Kresimir Romic +3 more
TL;DR: The types of learning problems commonly employed in medical image processing are introduced and an overview of commonly used deep learning methods are presented, including convolutional neural networks (CNNs), recurrent neural Networks (RNNs), and generative adversarial networks (GANs), with a focus on the image analysis task they are solving.
29
Deep Learning for Optical Sensor Applications: A Review
18 Jul 2023
TL;DR: In this article , the authors present recent studies integrating DL algorithms with optical sensor applications and highlight several directions for DL algorithms that promise a considerable impact on use for optical sensors applications, and provide new directions for the future development of related research.
16
References
A Survey of Recommender Systems Based on Deep Learning
TL;DR: This paper provides a comprehensive review of the related research contents of deep learning-based recommender systems and introduces the basic terminologies and the background concepts of recommender system and deep learning technology.
Feature learning based on SAE–PCA network for human gesture recognition in RGBD images
TL;DR: A feature learning approach based on sparse auto-encoder (SAE) and principle component analysis is proposed for recognizing human actions, i.e. finger-spelling or sign language, for RGB-D inputs, which improves the recognition rate from 75% to 99.05% and outperforms the state-of-the-art.
Deep Learning: Methods and Applications
Li Deng,Dong Yu +1 more
TL;DR: This monograph provides an overview of deep learning methodology and its applications in signal and information processing tasks, including speech recognition, computer vision, natural language processing, and multimodal information processing.
•Proceedings Article
A Deep Learning Approach to Link Prediction in Dynamic Networks.
Xiaoyi Li,Nan Du,Hui Li,Kang Li,Jing Gao,Aidong Zhang +5 more
- 01 Jan 2014
TL;DR: A novel deep learning framework, i.e., Conditional Temporal Restricted Boltzmann Machine (ctRBM), which predicts links based on individual transition variance as well as influence introduced by local neighbors is proposed, which outperforms existing algorithms in link inference on dynamic networks.
Deep autoencoder neural networks for gene ontology annotation predictions
Davide Chicco,Peter Sadowski,Pierre Baldi +2 more
- 20 Sep 2014
TL;DR: With experiments on gene annotation data from the Gene Ontology project, it is shown that deep autoencoder networks achieve better performance than other standard machine learning methods, including the popular truncated singular value decomposition.