diffusion models
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What is diffusion models?
Diffusion models are a class of generative models that learn to generate data by gradually reversing a diffusion process. This process starts with adding noise to the data until it becomes pure noise, and then learning to reverse this process to generate new data samples from the noise. They are commonly used for image generation, audio synthesis, and other tasks where generating high-quality and diverse samples is important.
What other technologies are related to diffusion models?
diffusion models Competitor Technologies
GANs
Generative Adversarial Networks are an alternative generative modeling technique to diffusion models, using a discriminator and generator network.
VAEs
Variational Autoencoders are another approach to generative modeling, using an encoder-decoder structure to learn latent representations and generate new samples. They are a common alternative to diffusion models.
NeRFs
Neural Radiance Fields offer an alternative approach to 3D scene representation and novel view synthesis, thus competing with diffusion models in generative tasks related to 3D content.
Variational Autoencoders
Variational Autoencoders are another approach to generative modeling, using an encoder-decoder structure to learn latent representations and generate new samples. They are a common alternative to diffusion models.
Generative Adversarial Networks
Generative Adversarial Networks are an alternative generative modeling technique to diffusion models, using a discriminator and generator network.
NeRF
Neural Radiance Fields offer an alternative approach to 3D scene representation and novel view synthesis, thus competing with diffusion models in generative tasks related to 3D content.
VAE
Variational Autoencoders are another approach to generative modeling, using an encoder-decoder structure to learn latent representations and generate new samples. They are a common alternative to diffusion models.
Number of organizations that mention technology
713
diffusion models
1,435
GANs
658
VAEs
78
NeRFs
175
Variational Autoencoders
341
Generative Adversarial Networks
194
NeRF
314
VAE
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diffusion models Complementary Technologies
Transformers
Transformers are often used within diffusion models, especially for tasks like image generation, acting as the backbone architecture for processing and generating data.
PyTorch
PyTorch is a popular deep learning framework used to implement and train diffusion models.
LLMs
Diffusion models can be used with LLMs for various generative tasks, such as text-to-image generation, making them complementary.
Number of organizations that mention technology
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Which job functions commonly mention diffusion models?
Applied Scientist
1.0% of all Applied Scientist jobs mention diffusion models
View 112
jobs on Sumble
Research Scientist
0.5% of all Research Scientist jobs mention diffusion models
View 245
jobs on Sumble
Machine Learning
0.4% of all Machine Learning jobs mention diffusion models
View 470
jobs on Sumble
AI Engineer
0.4% of all AI Engineer jobs mention diffusion models
View 339
jobs on Sumble
Data, Analytics & Machine Learning
0.1% of all Data, Analytics & Machine Learning jobs mention diffusion models
View 1k
jobs on Sumble
Which organizations are mentioning diffusion models?
Google
Scientific and Technical Services
Microsoft
Scientific and Technical Services
Apple
Scientific and Technical Services
Adobe
Scientific and Technical Services
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