Few-Shot Skull Images for Generative Adversarial Network Training

Skull images used for research on few-shot image synthesis with Generative Adversarial Networks (GANs). It is associated with the 2021 arXiv paper 'Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis' by Bingchen Liu et al. The dataset is hosted on Hugging Face and was last updated in April 2022.

Use Cases

• Train or benchmark few-shot GAN models for high-fidelity skull image synthesis.
• Research image modality-specific challenges in stabilizing GAN training with limited data.
• Apply transfer learning from models trained on this skull image collection to other medical or anatomical domains.

Strengths

• Dataset is directly linked to a peer-reviewed arXiv research paper from 2021.
• Hosted on the Hugging Face platform with standardized tags for modality (image) and region (us).
• Last update recorded as April 12, 2022, providing a timestamp for versioning.

Limitations

• Specific details like image count, resolution, and annotation labels are not provided in the input.
• The dataset's size category is listed as 'n1 K', suggesting it may contain only around 1,000 images, which is small for many deep learning tasks.
• Geographic coverage is tagged as 'us', which may introduce a regional bias in the skull imagery.

Provenance

Source

huggan on Hugging Face, associated with the arXiv paper abs/2101.04775.

Collection Method

Collection method is not specified; likely curated for the cited research on GANs.

Freshness

Last updated on 2022-04-12.

Geography

Tagged as 'Regionus', suggesting United States origin.