Summary of Deep Generative Sampling in the Dual Divergence Space: a Data-efficient & Interpretative Approach For Generative Ai, by Sahil Garg and Anderson Schneider and Anant Raj and Kashif Rasul and Yuriy Nevmyvaka and Sneihil Gopal and Amit Dhurandhar and Guillermo Cecchi and Irina Rish
Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI
by Sahil Garg, Anderson Schneider, Anant Raj, Kashif Rasul, Yuriy Nevmyvaka, Sneihil Gopal, Amit Dhurandhar, Guillermo Cecchi, Irina Rish
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Information Theory (cs.IT)
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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 This paper proposes a novel challenge in generative modeling, aiming to generate multivariate time series that resemble images. The authors address the issue of small sample sizes by developing an information theory-based method that characterizes the distribution of images and allows for direct sampling in the optimized dual space. They achieve this by estimating the KL-divergence between the data distribution and its marginal distribution. This approach enables efficient generative modeling with reduced sample complexity. The authors demonstrate the effectiveness of their method through extensive empirical evaluation using real-world datasets from various domains, outperforming state-of-the-art deep learning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make computers generate images that look like real pictures. Normally, computers are very bad at this because they only have a few examples to work with. But the authors of this paper came up with a clever idea to use information theory to make the computer learn from just a few examples. They tested their method on many different kinds of data and found that it worked better than other methods that need more examples. This could be very useful for things like generating images for medical records or creating realistic weather forecasts. |
Keywords
* Artificial intelligence * Deep learning * Time series
