VAE: Variational Auto-Encoder (2013)
Ever stumbled upon the Auto-Encoding Variational Bayes paper and felt lost amid mathematical equations and concepts like Auto-Encoder, Bayesian Theorem, Variational Inference, and Deep Learning? Understanding this groundbreaking work is a challenge but a rewarding journey that unveils new perspectives in machine learning. Variational Auto-Encoders, at the core of this paper, are powerful tools that replicate input data and generate new outputs. If these concepts intrigue you and you want to uncover the insights behind the AEVB paper, this article is for you.
Variational Auto-Encoders, commonly known as VAEs, are powerful tools in machine learning. Unlike traditional Auto-Encoder models that primarily replicate input data, VAEs can generate new outputs. They achieve this by identifying and learning hidden features from training datasets and using them as a blueprint to generate new data.
Diederik P Kingma and Max Welling from the University of Amsterdam significantly advanced this field with their Auto-Encoding Variational Bayes paper. This pioneering work introduced AEVB, a novel approach to training generative models. AEVB’s name symbolizes two crucial functions:
- ‘AE’ for compressing and reconstructing data
