Summary of Copyright-aware Incentive Scheme For Generative Art Models Using Hierarchical Reinforcement Learning, by Zhuan Shi et al.
Copyright-Aware Incentive Scheme for Generative Art Models Using Hierarchical Reinforcement Learning
by Zhuan Shi, Yifei Song, Xiaoli Tang, Lingjuan Lyu, Boi Faltings
First submitted to arxiv on: 26 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT)
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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 approach addresses the growing concern of copyright infringement in generative art by introducing a novel copyright metric grounded in copyright law and court precedents on infringement. The TRAK method is employed to estimate data holders’ contributions, with a hierarchical budget allocation method based on reinforcement learning determining remuneration for each round. Experimental results across three datasets demonstrate the effectiveness of this approach in optimizing budget distribution while protecting copyrights. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative art using Diffusion models has become increasingly popular, but it raises concerns about copyright infringement. The problem is that models can produce images very similar to copyrighted works without permission. To solve this issue, researchers have tried to modify the model or pay data holders for their contributions, but these methods don’t fully address the problem. A new approach introduces a special metric to measure copyright infringement and uses a method called TRAK to estimate how much each data holder contributed. The budget is then divided into rounds, with a system that rewards data holders based on their contribution and the amount of copyright loss in each round. This approach was tested across three datasets and showed better results than previous methods. |
Keywords
» Artificial intelligence » Diffusion » Reinforcement learning
