Summary of Latent-based Diffusion Model For Long-tailed Recognition, by Pengxiao Han et al.
Latent-based Diffusion Model for Long-tailed Recognition
by Pengxiao Han, Changkun Ye, Jieming Zhou, Jing Zhang, Jie Hong, Xuesong Li
First submitted to arxiv on: 6 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach called Latent-based Diffusion Model for Long-tailed Recognition (LDMLR) to tackle long-tailed imbalance distribution in computer vision applications. The LDMLR combines re-sampling, re-weighting, transfer learning, and feature augmentation methods by encoding the imbalanced dataset into features using a baseline model, generating pseudo-features through Denoising Diffusion Implicit Model (DDIM), and training a classifier with both encoded and pseudo-features. This approach demonstrates improved accuracy on CIFAR-LT and ImageNet-LT datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is trying to solve a problem in computer vision where some classes have many more examples than others. They’re proposing a new way to do this using something called diffusion models. It’s like training a machine to generate fake data that looks like the real thing, and then using that fake data to help the computer learn better. They tested it on two big datasets and it worked well. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Transfer learning
