Split-AE: An Autoencoder-based Disentanglement Framework for 3D Shape-to-shape Feature Transfer

Saha, S. K.; Minku, Leandro L.; Yao, Xin; Sendhoff, Bernhard; Menzel, Stefan

doi:10.1109/ijcnn55064.2022.9892814

Cited by 1 publication

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Achieving a disentangled latent representation that separates various factors of variation is a challenge. Recent research introduces methods like Split-AE (Saha et al 2022) and 3D Shape Variational Autoencoder Latent Disentanglement (Foti et al 2022), addressing this challenge. Other approaches employ deep learning features for 3D shape retrieval by projecting 3D shapes into 2D space and utilizing AEs for feature learning (Zhu et al 2016).…”

Section: Machine Visionmentioning

confidence: 99%

Autoencoders and their applications in machine learning: a survey

Berahmand,

Daneshfar,

Salehi

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

Autoencoders have become a hot researched topic in unsupervised learning due to their ability to learn data features and act as a dimensionality reduction method. With rapid evolution of autoencoder methods, there has yet to be a complete study that provides a full autoencoders roadmap for both stimulating technical improvements and orienting research newbies to autoencoders. In this paper, we present a comprehensive survey of autoencoders, starting with an explanation of the principle of conventional autoencoder and their primary development process. We then provide a taxonomy of autoencoders based on their structures and principles and thoroughly analyze and discuss the related models. Furthermore, we review the applications of autoencoders in various fields, including machine vision, natural language processing, complex network, recommender system, speech process, anomaly detection, and others. Lastly, we summarize the limitations of current autoencoder algorithms and discuss the future directions of the field.

show abstract

Section: Machine Visionmentioning

confidence: 99%