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VAEs

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**VAEs**

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.

Find 658 organizations using VAEs on Sumble →

What other technologies are related to VAEs?

VAEs Competitor Technologies

GANs
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.
diffusion models
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.
autoregressive models
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.
NF
NF
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.
Stable Diffusion
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.
DALL-E
DALL-E models are image generation models. They offer similar results and are therefore a competitor.
Number of organizations that mention technology
658
VAEs
1,435
GANs
713
diffusion models
74
autoregressive models
116
NF
1,167
Stable Diffusion
913
DALL-E
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VAEs Complementary Technologies

CNNs
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.
Transformers
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.
TensorFlow
TensorFlow
TensorFlow is a deep learning framework that can be used to implement and train VAE models.
Number of organizations that mention technology
658
VAEs
1,041
CNNs
5,962
Transformers
30,502
TensorFlow
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Which job functions commonly mention VAEs?

Applied Scientist
1.1% of all Applied Scientist jobs mention VAEs
View 123 jobs on Sumble
AI Engineer
0.4% of all AI Engineer jobs mention VAEs
View 367 jobs on Sumble
Machine Learning
0.2% of all Machine Learning jobs mention VAEs
View 216 jobs on Sumble
Data Scientist
0.1% of all Data Scientist jobs mention VAEs
View 308 jobs on Sumble
Data, Analytics & Machine Learning
0.1% of all Data, Analytics & Machine Learning jobs mention VAEs
View 922 jobs on Sumble
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