VAEs
VAEs (Variational Autoencoders) are a class of generative models used in machine learning to learn efficient representations of data, often for generating new data samples that resemble the input data. VAEs combine principles from autoencoders and probabilistic graphical models, learning to encode data into a lower-dimensional latent space and then decoding it back into its original form. They are widely used in applications like image generation, anomaly detection, and semi-supervised learning. This tag is valuable for researchers and practitioners looking to explore how VAEs can be applied to solve complex problems in data generation and representation. Engaging with VAEs helps deepen understanding of probabilistic modeling in deep learning.
