Few-Shot Art Painting Images for Generative Model Training

A collection of images of art paintings intended for few-shot image synthesis research. It is associated with a 2021 arXiv paper on faster and stabilized GAN training for high-fidelity few-shot image synthesis. The dataset size and specific column structure are not provided in the input.

Use Cases

• Train generative adversarial networks (GANs) for few-shot image synthesis using the provided art painting images.
• Benchmark few-shot learning algorithms on the modality of art painting images.
• Study the stabilization of GAN training for high-fidelity synthesis as referenced in the associated arXiv paper.

Strengths

• Associated with a peer-reviewed arXiv paper (arXiv:2101.04775) published in 2021.
• Tagged with a size category indicating it contains between 1,000 and 10,000 samples (Size Categories1 Kn10 K).
• Stored in the Parquet format, which is efficient for columnar data storage.

Limitations

• The exact number of rows (images) and columns (features) is unknown, making it difficult to assess scale and structure.
• The dataset's last known update was in April 2022, which may limit its use for evaluating the most recent generative models.
• Specific license, author organization, and detailed data collection methodology are not provided.

Provenance

Source

huggan on Hugging Face, associated with the arXiv paper 'Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis'.

Collection Method

Presumably collected for research on few-shot image synthesis, but the exact gathering method is not specified.

Freshness

Last updated on 2022-04-12.

Geography

Tagged with 'Regionus', but specific spatial coverage is not detailed.