Summary of Long Tail Image Generation Through Feature Space Augmentation and Iterated Learning, by Rafael Elberg et al.
Long Tail Image Generation Through Feature Space Augmentation and Iterated Learning
by Rafael Elberg, Denis Parra, Mircea Petrache
First submitted to arxiv on: 2 May 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 proposed method for image augmentation in long-tailed data leverages the rich latent space of pre-trained Stable Diffusion Models to address challenges in medical domain image and multimodal machine learning tasks. Specifically, it tackles issues in generating data from under-represented classes and slow inference process. The approach creates a modified separable latent space by mixing head and tail class examples through Iterated Learning of underlying sparsified embeddings. This method applies K-NN saliency maps to task-specific maps for accurate results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research proposes a new way to improve image generation quality in medical imaging tasks when data is scarce or private. The idea uses powerful Stable Diffusion Models and makes it easier to generate images from rare classes, which helps with more accurate diagnoses. This approach also speeds up the process of generating images. |
Keywords
» Artificial intelligence » Image generation » Inference » Latent space » Machine learning
