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Is Noise Conditioning Necessary for Denoising Generative Models?
Paper • 2502.13129 • Published • 1 -
REPA-E: Unlocking VAE for End-to-End Tuning with Latent Diffusion Transformers
Paper • 2504.10483 • Published • 21 -
Mean Flows for One-step Generative Modeling
Paper • 2505.13447 • Published • 7 -
Latent Diffusion Model without Variational Autoencoder
Paper • 2510.15301 • Published • 49
Bo Lin
linbo0518
AI & ML interests
None yet
Recent Activity
liked
a dataset
10 days ago
ILSVRC/imagenet-1k
updated
a collection
about 2 months ago
ToRead
updated
a collection
about 2 months ago
ToRead
Organizations
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Diffusion Principle
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Generative Modeling by Estimating Gradients of the Data Distribution
Paper • 1907.05600 • Published -
Score-Based Generative Modeling through Stochastic Differential Equations
Paper • 2011.13456 • Published • 2 -
Elucidating the Design Space of Diffusion-Based Generative Models
Paper • 2206.00364 • Published • 18 -
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
Paper • 2209.03003 • Published • 2
Diffusion Language Models
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Structured Denoising Diffusion Models in Discrete State-Spaces
Paper • 2107.03006 • Published • 1 -
Simplified and Generalized Masked Diffusion for Discrete Data
Paper • 2406.04329 • Published • 8 -
Simple and Effective Masked Diffusion Language Models
Paper • 2406.07524 • Published • 12 -
Large Language Diffusion Models
Paper • 2502.09992 • Published • 123
Local Feature
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SuperPoint: Self-Supervised Interest Point Detection and Description
Paper • 1712.07629 • Published -
D2-Net: A Trainable CNN for Joint Detection and Description of Local Features
Paper • 1905.03561 • Published -
R2D2: Repeatable and Reliable Detector and Descriptor
Paper • 1906.06195 • Published -
SuperGlue: Learning Feature Matching with Graph Neural Networks
Paper • 1911.11763 • Published
ToRead
-
Is Noise Conditioning Necessary for Denoising Generative Models?
Paper • 2502.13129 • Published • 1 -
REPA-E: Unlocking VAE for End-to-End Tuning with Latent Diffusion Transformers
Paper • 2504.10483 • Published • 21 -
Mean Flows for One-step Generative Modeling
Paper • 2505.13447 • Published • 7 -
Latent Diffusion Model without Variational Autoencoder
Paper • 2510.15301 • Published • 49
Diffusion Language Models
-
Structured Denoising Diffusion Models in Discrete State-Spaces
Paper • 2107.03006 • Published • 1 -
Simplified and Generalized Masked Diffusion for Discrete Data
Paper • 2406.04329 • Published • 8 -
Simple and Effective Masked Diffusion Language Models
Paper • 2406.07524 • Published • 12 -
Large Language Diffusion Models
Paper • 2502.09992 • Published • 123
Diffusion Principle
-
Generative Modeling by Estimating Gradients of the Data Distribution
Paper • 1907.05600 • Published -
Score-Based Generative Modeling through Stochastic Differential Equations
Paper • 2011.13456 • Published • 2 -
Elucidating the Design Space of Diffusion-Based Generative Models
Paper • 2206.00364 • Published • 18 -
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
Paper • 2209.03003 • Published • 2
Local Feature
-
SuperPoint: Self-Supervised Interest Point Detection and Description
Paper • 1712.07629 • Published -
D2-Net: A Trainable CNN for Joint Detection and Description of Local Features
Paper • 1905.03561 • Published -
R2D2: Repeatable and Reliable Detector and Descriptor
Paper • 1906.06195 • Published -
SuperGlue: Learning Feature Matching with Graph Neural Networks
Paper • 1911.11763 • Published