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
Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data
TL;DR: The application of deep learning methods to organ identification in magnetic resonance medical images is tested, with visual and temporal hierarchical features learned to categorize object classes from an unlabeled multimodal DCE-MRI dataset so that only a weakly supervised training is required for a classifier.
533
Deep Collaborative Filtering via Marginalized Denoising Auto-encoder
Sheng Li,Jaya Kawale,Yun Fu +2 more
- 17 Oct 2015
TL;DR: A general deep architecture for CF is proposed by integrating matrix factorization with deep feature learning, which leads to a parsimonious fit over the latent features as indicated by its improved performance in comparison to prior state-of-art models over four large datasets for the tasks of movie/book recommendation and response prediction.
524
Facial expression recognition via learning deep sparse autoencoders
TL;DR: A novel framework for facial expression recognition to automatically distinguish the expressions with high accuracy is presented and a high recognition accuracy is achieved, which successfully demonstrates the feasibility and effectiveness of the approach.
516
Deep Feature Learning for Medical Image Analysis with Convolutional Autoencoder Neural Network
TL;DR: A convolutional autoencoder deep learning framework to support unsupervised image features learning for lung nodule through unlabeled data, which only needs a small amount of labeled data for efficient feature learning.
479
A review on deep learning for recommender systems: challenges and remedies
TL;DR: This study provides a comprehensive review of deep learning-based recommendation approaches to enlighten and guide newbie researchers interested in the subject.
478