DDPM - Diffusion Models Beat GANs on Image Synthesis (Machine Learning Research Paper Explained)

#ddpm #diffusionmodels #openai

GANs have dominated the image generation space for the majority of the last decade. This paper shows for the first time, how a non-GAN model, a DDPM, can be improved to overtake GANs at standard evaluation metrics for image generation. The produced samples look amazing and other than GANs, the new model has a formal probabilistic foundation. Is there a future for GANs or are Diffusion Models going to overtake them for good?

OUTLINE:
0:00 - Intro & Overview
4:10 - Denoising Diffusion Probabilistic Models
11:30 - Formal derivation of the training loss
23:00 - Training in practice
27:55 - Learning the covariance
31:25 - Improving the noise schedule
33:35 - Reducing the loss gradient noise
40:35 - Classifier guidance
52:50 - Experimental Results

Paper (this): https://arxiv.org/abs/2105.05233
Paper (previous): https://arxiv.org/abs/2102.09672
Code: https://github.com/openai/guided-diff...

Abstract:
We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for sample quality using gradients from a classifier. We achieve an FID of 2.97 on ImageNet 128×128, 4.59 on ImageNet 256×256, and 7.72 on ImageNet 512×512, and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution. Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.85 on ImageNet 512×512. We release our code at this https URL

Authors: Alex Nichol, Prafulla Dhariwal

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