Summary of Text2data: Low-resource Data Generation with Textual Control, by Shiyu Wang et al.
Text2Data: Low-Resource Data Generation with Textual Control
by Shiyu Wang, Yihao Feng, Tian Lan, Ning Yu, Yu Bai, Ran Xu, Huan Wang, Caiming Xiong, Silvio Savarese
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The proposed Text2Data approach addresses the challenge of generating data for low-resource areas with expensive annotations or complex data structures. By utilizing unlabeled data and an unsupervised diffusion model, Text2Data understands the underlying data distribution without requiring textual labels. This novel method is then finetuned using a constraint optimization-based learning objective to ensure controllability and prevent catastrophic forgetting. The results demonstrate enhanced performance for controlling data generation across various modalities, including molecules, motion dynamics, and time series. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Text2Data helps machines understand human language by generating data that matches written instructions. This is important because it lets people interact easily with machines. Right now, there’s a problem when trying to generate data in areas where it’s hard or expensive to get labeled information, like molecules or motion dynamics. The proposed approach uses unlabeled data and an unsupervised model to understand the underlying data distribution. It then refines this understanding using a special optimization technique that ensures the generated data is controllable. Tests show that Text2Data performs better than existing methods in controlling data generation for different types of data. |
Keywords
* Artificial intelligence * Diffusion model * Optimization * Time series * Unsupervised
