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
Masked Autoencoders As Spatiotemporal Learners
Christoph Feichtenhofer, Haoqi Fan, Yanghao Li, Kaiming He
Learning latent representations for operational nitrogen response rate prediction
Christos Pylianidis, Ioannis N. Athanasiadis
CARNet: A Dynamic Autoencoder for Learning Latent Dynamics in Autonomous Driving Tasks
Andrey Pak, Hemanth Manjunatha, Dimitar Filev, Panagiotis Tsiotras
Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition
Bruna Delazeri, Leonardo L. Veras, Alceu de S. Britto, Jean Paul Barddal, Alessandro L. Koerich
A Comparative Study on Approaches to Acoustic Scene Classification using CNNs
Ishrat Jahan Ananya, Sarah Suad, Shadab Hafiz Choudhury, Mohammad Ashrafuzzaman Khan