Summary of Your Image Is Secretly the Last Frame Of a Pseudo Video, by Wenlong Chen et al.
Your Image is Secretly the Last Frame of a Pseudo Video
by Wenlong Chen, Wenlin Chen, Lapo Rastrelli, Yingzhen Li
First submitted to arxiv on: 26 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the reasons behind the success of diffusion models in generating high-quality images, which are typically considered a special case of hierarchical variational autoencoders (HVAEs). The authors hypothesize that the key to this success lies in the additional self-supervision information provided by corrupted images, which form pseudo videos along with the original image. Building on this idea, they investigate whether other generative models can be improved using similar pseudo videos. Specifically, they extend a given image generator to its video counterpart and train it on pseudo videos constructed through data augmentation. The authors also analyze potential issues with first-order Markov data augmentation methods used in diffusion models and propose more expressive approaches. Experimental results on the CIFAR10 and CelebA datasets demonstrate that incorporating self-supervised information from pseudo videos can lead to improved image generation quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at why some computer programs called “diffusion models” are really good at making realistic pictures. They think it’s because these models get extra help from fake videos made by changing the original pictures a little bit. The researchers wonder if they can make other picture-making programs better by using these fake videos too. So, they took an existing picture-maker and turned it into a video-maker that uses these fake videos as training data. They also looked at some ways to improve this process. When they tested their ideas on two big datasets, they found that making pictures got even better when they used the fake videos! |
Keywords
» Artificial intelligence » Data augmentation » Diffusion » Image generation » Self supervised
