Summary of Generative Expansion Of Small Datasets: An Expansive Graph Approach, by Vahid Jebraeeli et al.
Generative Expansion of Small Datasets: An Expansive Graph Approach
by Vahid Jebraeeli, Bo Jiang, Hamid Krim, Derya Cansever
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Expansive Synthesis model is introduced, capable of generating large-scale, information-rich datasets from minimal samples. By leveraging neural networks’ non-linear latent space and expander graph mappings, the model preserves data distribution and feature relationships. The technique combines a Koopman operator to create a linear feature space for dataset expansion, an autoencoder with self-attention layers for refining distributional consistency, and optimal transport for further refinement. The proposed method demonstrates comparable performance to classifiers trained on original datasets, making it a promising solution for addressing data scarcity in machine learning applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is found to create more training data without needing much information. This helps solve the problem of limited data in machine learning, which can make models less accurate. The new method uses special graphs and features from neural networks to keep the data’s original patterns and relationships. It also uses autoencoders and a technique called optimal transport to make sure the generated data is similar to real data. Tests show that this approach works well, making it useful for projects where there isn’t enough data. |
Keywords
» Artificial intelligence » Autoencoder » Latent space » Machine learning » Self attention
