Summary of Zero-forget Preservation Of Semantic Communication Alignment in Distributed Ai Networks, by Jingzhi Hu et al.
Zero-Forget Preservation of Semantic Communication Alignment in Distributed AI Networks
by Jingzhi Hu, Geoffrey Ye Li
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 zero-forget domain adaptation (ZFDA) framework addresses the issue of preserving semantic communication (SC) alignment when artificial intelligence (AI) adapts to different domains. By designing sparse additive modifications (SAM) to AI neural parameters, which can be efficiently stored and switched-off, ZFDA prevents distortion of SC alignment. The SAM is optimized by decoupling it into continuous variables and a binary mask, handled via score-based optimization. Experimental results on image transmission show that the proposed framework preserves SC alignment with minimal loss of domain adaptation performance at a low memory cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI’s ability to adapt to different domains can distort semantic communication (SC) alignment. The zero-forget domain adaptation (ZFDA) framework solves this problem by adding sparse modifications to AI neural parameters, which are then stored and switched-off to restore SC alignment. This approach optimizes the modifications using a score-based method. Tests with image transmission show that ZFDA preserves SC alignment while still allowing for good domain adaptation. |
Keywords
» Artificial intelligence » Alignment » Domain adaptation » Mask » Optimization » Sam
