Summary of The Bid Picture: Auction-inspired Multi-player Generative Adversarial Networks Training, by Joo Yong Shim et al.
The Bid Picture: Auction-Inspired Multi-player Generative Adversarial Networks Training
by Joo Yong Shim, Jean Seong Bjorn Choe, Jong-Kook Kim
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents a novel approach to address mode collapse in Generative Adversarial Networks (GANs). Mode collapse occurs when a GAN overfits and generates limited samples that are concentrated on a small subset of the data distribution. Despite this restricted diversity, the discriminator can still be deceived into distinguishing these samples as real. To mitigate this issue, the authors propose auction-inspired multi-player generative adversarial networks training, which extends the traditional two-player game to multiple players. During training, each model’s value is determined by bids submitted by other players in an auction-like process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about finding a way to make GANs generate more diverse and realistic samples. Right now, when GANs overfit, they tend to produce limited variations of the same thing. This new approach involves multiple models working together and trying to outdo each other in an “auction” style competition. The goal is to create more varied and realistic data that can be used for various applications. |
Keywords
* Artificial intelligence * Gan
