Summary of Ctg-krew: Generating Synthetic Structured Contextually Correlated Content by Conditional Tabular Gan with K-means Clustering and Efficient Word Embedding, By Riya Samanta et al.
CTG-KrEW: Generating Synthetic Structured Contextually Correlated Content by Conditional Tabular GAN with K-Means Clustering and Efficient Word Embedding
by Riya Samanta, Bidyut Saha, Soumya K. Ghosh, Sajal K. Das
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 Conditional Tabular Generative Adversarial Networks (CTGAN) and its derivatives have gained popularity for generating synthetic tabular data efficiently and flexibly, showcasing strong performance and adaptability. However, traditional approaches face two critical limitations: they cannot preserve semantic integrity of contextually correlated words or phrases, and require significant memory resources and CPU time during training. To address these issues, the authors introduce CTGKrEW (Conditional Tabular GAN with KMeans Clustering and Word Embedding), a novel framework that generates realistic synthetic tabular data where attributes are collections of semantically coherent words. The proposed framework is trained and evaluated on a dataset from Upwork, a real-world freelancing platform, and outperforms the conventional approach in terms of CPU time (99% less) and memory footprints (33% less). Additionally, KrEW, a web application, is developed to facilitate realistic data generation containing skill-related information. This application is freely accessible at this https URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Conditional Tabular Generative Adversarial Networks are special kinds of computer programs that can create fake data sets quickly and easily. These programs have some big advantages, but they also have two major problems. The first problem is that they don’t do a good job of keeping track of the relationships between different pieces of information. For example, if you’re trying to generate fake profiles for freelancers on a website, these programs might not understand that skills like “programming” and “data analysis” are related to each other. The second problem is that these programs use up a lot of computer power and memory when they’re learning how to create fake data. To solve these problems, the researchers created a new program called CTGKrEW that does a better job of understanding relationships between pieces of information and uses less computer power and memory. |
Keywords
» Artificial intelligence » Clustering » Embedding » Gan
