Adversarial AutoEncoder

Adversarial autoencoders (AAEs) are a class of generative models that leverage adversarial training to learn efficient data representations and generate new data samples. Current research focuses on enhancing AAE performance through architectural innovations, such as incorporating self-attention mechanisms, LSTMs for time series data, and integrating them with other techniques like GANs and diffusion models, to improve data reconstruction, anomaly detection, and generation quality. AAEs find applications across diverse fields, including anomaly detection in cybersecurity and medical imaging, optimization problems in quantum computing, and data augmentation for improving the performance of machine learning models in various domains. Their ability to learn complex data distributions and generate realistic synthetic data makes them a valuable tool for numerous scientific and engineering applications.

Papers

July 18, 2024

Non-native Quantum Generative Optimization with Adversarial Autoencoders
Blake A. Wilson, Jonathan Wurtz, Vahagn Mkhitaryan, Michael Bezick, Sheng-Tao Wang, Sabre Kais, Vladimir M. Shalaev, Alexandra Boltasseva
Large Scale Optimization Adversarial AutoEncoder Quantum Generative Quantum Autoencoders Quantum Boltzmann Machine Neutral Atom

May 9, 2024

Exploiting Autoencoder's Weakness to Generate Pseudo Anomalies
Marcella Astrid, Muhammad Zaigham Zaheer, Djamila Aouada, Seung-Ik Lee
Anomaly Detection Supervised Autoencoder Anomalous Data Anomalous Image Anomalous Event Adversarial AutoEncoder Physical Weakness

April 6, 2024

Beyond the Known: Adversarial Autoencoders in Novelty Detection
Muhammad Asad, Ihsan Ullah, Ganesh Sistu, Michael G. Madden
Neural Network Novelty Detection Adversarial AutoEncoder Inlier Classification Novelty Reaction Kernel Based Entropic Novelty

March 31, 2024

An Unsupervised Adversarial Autoencoder for Cyber Attack Detection in Power Distribution Grids
Mehdi Jabbari Zideh, Mohammad Reza Khalghani, Sarika Khushalani Solanki
Power Grid Adversarial AutoEncoder Cyber Attack Detection Distribution Grid Historical Load Unbalanced Power Distribution Grid

February 27, 2024

Thermodynamics-informed super-resolution of scarce temporal dynamics data
Carlos Bermejo-Barbanoj, Beatriz Moya, Alberto Badías, Francisco Chinesta, Elías Cueto
Generative Model Super Resolution Adversarial AutoEncoder Preserving Loss

January 18, 2024

Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network
Qiong Wu, Wenhua Wang, Pingyi Fan, Qiang Fan, Huiling Zhu, Khaled B. Letaief
Federated Prompt Cooperation Multi Agent Deep Reinforcement Learning Base Station Adversarial AutoEncoder Next Generation Network Edge Caching

December 18, 2023

Time-Transformer: Integrating Local and Global Features for Better Time Series Generation
Yuansan Liu, Sudanthi Wijewickrema, Ang Li, Christofer Bester, Stephen O'Leary, James Bailey
Temporal Convolutional Network Global Feature Time Series Generation Adversarial AutoEncoder Time Transformer

August 30, 2023

Fully Embedded Time-Series Generative Adversarial Networks
Joe Beck, Subhadeep Chakraborty
Generative Adversarial Network Synthetic Data Adversarial Training Time Series Adversarial AutoEncoder

July 31, 2023

Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training
Jeya Maria Jose Valanarasu, Yucheng Tang, Dong Yang, Ziyue Xu, Can Zhao, Wenqi Li, Vishal M. Patel, Bennett Landman, Daguang Xu, Yufan He, Vishwesh Nath
Supervised ImageNet Cross Modal 3D Medical Image Level Representation Feature Masking Low Level Feature Adversarial AutoEncoder

July 19, 2023

Adversarial Latent Autoencoder with Self-Attention for Structural Image Synthesis
Jiajie Fan, Laure Vuaille, Hao Wang, Thomas Bäck
Self Attention Image Synthesis Generative Design Adversarial AutoEncoder

June 21, 2023

Chili Pepper Disease Diagnosis via Image Reconstruction Using GrabCut and Generative Adversarial Serial Autoencoder
Jongwook Si, Sungyoung Kim
Image Reconstruction Score Based Plant Disease Adversarial AutoEncoder Image Based Score GAN Structure

June 8, 2023

Unscented Autoencoder
Faris Janjoš, Lars Rosenbaum, Maxim Dolgov, J. Marius Zöllner
Variational Autoencoder Unscented Kalman Filter Adversarial AutoEncoder Posterior Network Latent Posterior

May 14, 2023

Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning
Mina Razghandi, Hao Zhou, Melike Erol-Kantarci, Damla Turgut
Q Learning Smart Environment Synthetic Data Generator Adversarial AutoEncoder Home Energy Management

September 5, 2022

Representation Learning for Non-Melanoma Skin Cancer using a Latent Autoencoder
Simon Myles Thomas
Generative Model Representation Learning Supervised Autoencoder Generative Learning Skin Cancer Adversarial AutoEncoder

June 29, 2022

DrumGAN VST: A Plugin for Drum Sound Analysis/Synthesis With Autoencoding Generative Adversarial Networks
Javier Nistal, Cyran Aouameur, Ithan Velarde, Stefan Lattner
Generative Adversarial Network GAN Model Critical Synthesis Generic Plugin Adversarial AutoEncoder Drum Sound Drum Transcription

April 26, 2022

Designing thermal radiation metamaterials via hybrid adversarial autoencoder and Bayesian optimization
Dezhao Zhu, Jiang Guo, Gang Yu, C. Y. Zhao, Hong Wang, Shenghong Ju
Bayesian Optimization Optimal Design Adversarial AutoEncoder Material Informatics

February 19, 2022

Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders
Huangjie Zheng, Pengcheng He, Weizhu Chen, Mingyuan Zhou
Diffusion Based Generative Model Reverse Diffusion Text Guided Image Generation Adversarial AutoEncoder Forward Diffusion

January 18, 2022

Autoencoding Video Latents for Adversarial Video Generation
Sai Hemanth Kasaraneni
Supervised Autoencoder Video Generation Autoencoder Based Adversarial AutoEncoder

December 20, 2021

Turbo-Sim: a generalised generative model with a physical latent space
Guillaume Quétant, Mariia Drozdova, Vitaliy Kinakh, Tobias Golling, Slava Voloshynovskiy
Generative Adversarial Network Latent Space Adversarial AutoEncoder

November 26, 2021

Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley
Timely Survey Generative Adversarial Network Tutorial Review Adversarial AutoEncoder

Adversarial AutoEncoder

Papers

Non-native Quantum Generative Optimization with Adversarial Autoencoders

Exploiting Autoencoder's Weakness to Generate Pseudo Anomalies

Beyond the Known: Adversarial Autoencoders in Novelty Detection

An Unsupervised Adversarial Autoencoder for Cyber Attack Detection in Power Distribution Grids

Thermodynamics-informed super-resolution of scarce temporal dynamics data

Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network

Time-Transformer: Integrating Local and Global Features for Better Time Series Generation

Fully Embedded Time-Series Generative Adversarial Networks

Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training

Adversarial Latent Autoencoder with Self-Attention for Structural Image Synthesis

Chili Pepper Disease Diagnosis via Image Reconstruction Using GrabCut and Generative Adversarial Serial Autoencoder

Unscented Autoencoder

Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning

Representation Learning for Non-Melanoma Skin Cancer using a Latent Autoencoder

DrumGAN VST: A Plugin for Drum Sound Analysis/Synthesis With Autoencoding Generative Adversarial Networks

Designing thermal radiation metamaterials via hybrid adversarial autoencoder and Bayesian optimization

Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders

Autoencoding Video Latents for Adversarial Video Generation

Turbo-Sim: a generalised generative model with a physical latent space

Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey