Autoencoding

Autoencoding is a type of neural network technique used for unsupervised learning, dimensionality reduction, and generative modeling, where a model learns to compress and reconstruct its input data. As a fundamental concept in deep learning research, autoencoding has numerous applications in image and signal processing, anomaly detection, and data imputation, making it a crucial area of study for researchers and practitioners seeking to improve the efficiency and accuracy of their machine learning models.