arXiv (All)
open access
Generative Expansion of Small Datasets: An Expansive Graph Approach
Limited data availability in machine learning significantly impacts performance and generalization. Traditional augmentation methods enhance moderately sufficient datasets. GANs struggle with convergence when generating diverse samples. Diffusion models, while effective, have high computational cost
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Two-Step Data Augmentation for Masked Face Detection and Recognition: Turning Fake Masks to Real#1142596
- Beyond nearest neighbor interpolation in data augmentation#406925
- Data-Driven Stress Testing of Intermodal Freight Networks Using GAN-Generated Disruption Scenarios#1123738
- Graph Data Augmentation via Contrastive Generator Inversion ($\texttt{DCBA}$)#1201666
- Generative Adversarial Networks (GANs): A Survey of Techniques and Real-World Applications#74994
Record · ID 1389041
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive