Supervised Autoencoder

Supervised autoencoders are neural networks trained to reconstruct input data (e.g., images, time series, 3D models) via a compressed latent representation, often used for dimensionality reduction, feature extraction, and anomaly detection. Current research emphasizes developing novel architectures like Kolmogorov-Arnold Networks and hierarchical autoencoders, and integrating autoencoders with other techniques such as diffusion models and contrastive learning to improve reconstruction quality and downstream task performance. This approach finds applications across diverse fields, from improving network throughput in autonomous vehicles to enhancing image generation and analysis in astronomy and medical imaging, demonstrating the broad utility of supervised autoencoders in data processing and analysis.

Papers

July 7, 2022

Machine Learning to Predict Aerodynamic Stall
Ettore Saetta, Renato Tognaccini, Gianluca Iaccarino
Machine Learning Supervised Autoencoder Convolutional Autoencoder Airfoil Design Hypersonic Flow Pressure Map Stall Number

July 5, 2022

Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR Images
Ahmed Ghorbel, Ahmed Aldahdooh, Shadi Albarqouni, Wassim Hamidouche
Anomaly Detection Full Model Transformer Based Supervised Autoencoder Unsupervised Anomaly Detection Anomaly Segmentation Brain MR Image Unsupervised Anomaly Segmentation

July 4, 2022

AutoSpeed: A Linked Autoencoder Approach for Pulse-Echo Speed-of-Sound Imaging for Medical Ultrasound
Farnaz Khun Jush, Markus Biele, Peter M. Dueppenbecker, Andreas Maier
Deep Neural Network Supervised Autoencoder Radio Frequency Quantitative Ultrasound

July 3, 2022

Anomaly Detection with Adversarially Learned Perturbations of Latent Space
Vahid Reza Khazaie, Anthony Wong, John Taylor Jewell, Yalda Mohsenzadeh
Anomaly Detection Latent Space Supervised Autoencoder Unsupervised Anomaly Detection Adversarial Framework

July 1, 2022

June 23, 2022

Self-Supervised Training with Autoencoders for Visual Anomaly Detection
Alexander Bauer, Shinichi Nakajima, Klaus-Robert Müller
Anomaly Detection Supervised Autoencoder Visual Anomaly Detection Self Supervised Training Power Regularization

June 21, 2022

Multi-Omic Data Integration and Feature Selection for Survival-based Patient Stratification via Supervised Concrete Autoencoders
Pedro Henrique da Costa Avelar, Roman Laddach, Sophia Karagiannis, Min Wu, Sophia Tsoka
Supervised Autoencoder Feature Selection Multi Omics Data Multi Omics Concrete Autoencoder

June 15, 2022

June 10, 2022

Masked Autoencoders are Robust Data Augmentors
Haohang Xu, Shuangrui Ding, Xiaopeng Zhang, Hongkai Xiong, Qi Tian
Data Augmentation Supervised Autoencoder Masked Autoencoders Image Modeling Image Augmentation Robust Data

June 3, 2022

May 31, 2022

May 23, 2022

PCA-Boosted Autoencoders for Nonlinear Dimensionality Reduction in Low Data Regimes
Muhammad Al-Digeil, Yuri Grinberg, Daniele Melati3, Mohsen Kamandar Dezfouli, Jens H. Schmid, Pavel Cheben, Siegfried Janz, Dan-Xia Xu
Supervised Autoencoder Dimensionality Reduction Principal Component Analysis Low Data Regime Robust Initialization

Supervised Autoencoder

Papers

Machine Learning to Predict Aerodynamic Stall

Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR Images

AutoSpeed: A Linked Autoencoder Approach for Pulse-Echo Speed-of-Sound Imaging for Medical Ultrasound

Anomaly Detection with Adversarially Learned Perturbations of Latent Space

Masked Autoencoder for Self-Supervised Pre-training on Lidar Point Clouds

Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification

Attention-Guided Autoencoder for Automated Progression Prediction of Subjective Cognitive Decline with Structural MRI

Using Autoencoders on Differentially Private Federated Learning GANs

Data-driven reduced order models using invariant foliations, manifolds and autoencoders

Self-Supervised Training with Autoencoders for Visual Anomaly Detection

Multi-Omic Data Integration and Feature Selection for Survival-based Patient Stratification via Supervised Concrete Autoencoders

ARES: Locally Adaptive Reconstruction-based Anomaly Scoring

READ: Aggregating Reconstruction Error into Out-of-distribution Detection

Masked Autoencoders are Robust Data Augmentors

Is an encoder within reach?

Infinite Recommendation Networks: A Data-Centric Approach

Pre-training via Denoising for Molecular Property Prediction

Semantic Autoencoder and Its Potential Usage for Adversarial Attack

itKD: Interchange Transfer-based Knowledge Distillation for 3D Object Detection

PCA-Boosted Autoencoders for Nonlinear Dimensionality Reduction in Low Data Regimes