Tech Insights

VAEs

Last updated May 16, 2025, generated by Sumble

What is VAEs?

Variational Autoencoders (VAEs) are a type of generative model belonging to the family of neural networks. They learn a latent representation of input data and then generate new data points that are similar to the training data. Unlike standard autoencoders that learn a deterministic mapping, VAEs learn a probability distribution over the latent space, allowing for sampling new data points by sampling from this distribution and decoding it. They are commonly used for tasks such as image generation, data imputation, and representation learning.

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What other technologies are related to VAEs?

VAEs Competitor Technologies

GANs

Generative Adversarial Networks (GANs) are another type of generative model that can be used for similar tasks as VAEs, such as image generation and data augmentation. They offer an alternative approach to learning data distributions.

mentioned alongside VAEs in 42% (1.6k) of relevant job posts

diffusion models

Diffusion models are a more recent approach to generative modeling that are also used for image generation, audio synthesis and more. They offer an alternative to VAEs for modeling complex data distributions.

mentioned alongside VAEs in 19% (385) of relevant job posts

autoregressive models

Autoregressive models, such as PixelCNN, are another class of generative models. While less commonly used for the exact same tasks as VAEs, they represent an alternative approach to modeling probability distributions over high-dimensional data.

mentioned alongside VAEs in 65% (104) of relevant job posts

Normalizing Flows (NF) offer an alternative method for learning probability distributions and generating new samples. They transform a simple distribution into a complex one via a sequence of invertible mappings. While less commonly used than VAEs, they provide a competitive approach to generative modeling.

mentioned alongside VAEs in 22% (62) of relevant job posts

Stable Diffusion

Stable Diffusion is a latent diffusion model, a type of generative model that represents a competitor for some VAE applications, particularly image generation.

mentioned alongside VAEs in 3% (80) of relevant job posts

DALL-E

DALL-E models are image generation models. They offer similar results and are therefore a competitor.

mentioned alongside VAEs in 3% (59) of relevant job posts

VAEs Complementary Technologies

CNNs

Convolutional Neural Networks (CNNs) can be used as encoders and decoders within a VAE architecture, particularly for image data. They provide a way to extract features and reconstruct images within the VAE framework.

mentioned alongside VAEs in 7% (184) of relevant job posts

Transformers

Transformers can be used within VAE architectures, particularly for sequence data. They can also be used as components of the encoder or decoder networks, offering powerful sequence modeling capabilities.

mentioned alongside VAEs in 2% (533) of relevant job posts

TensorFlow

TensorFlow is a deep learning framework that can be used to implement and train VAE models.

mentioned alongside VAEs in 1% (1.2k) of relevant job posts