Summary of Mdm: Advancing Multi-domain Distribution Matching For Automatic Modulation Recognition Dataset Synthesis, by Dongwei Xu et al.
MDM: Advancing Multi-Domain Distribution Matching for Automatic Modulation Recognition Dataset Synthesis
by Dongwei Xu, Jiajun Chen, Yao Lu, Tianhao Xia, Qi Xuan, Wei Wang, Yun Lin, Xiaoniu Yang
First submitted to arxiv on: 5 Aug 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 The recent integration of deep learning into Automatic Modulation Recognition (AMR) tasks has been met with success, but only when trained on large-scale datasets. However, this reliance on extensive data poses significant storage and transmission challenges. To address these issues, researchers have developed methods for compressing large training datasets into smaller synthetic datasets that maintain performance. The unique characteristics of signals across various domains require specialized approaches for analysis and processing. A novel dataset distillation method, Multi-domain Distribution Matching (MDM), is proposed to tackle this challenge. MDM employs Discrete Fourier Transform (DFT) to translate time-domain signals into the frequency domain, using a model to compute distribution matching losses between synthetic and real datasets, considering both domains. Experimental results on three AMR datasets show that MDM achieves better performance under the same compression ratio compared to baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A recent breakthrough in deep learning has improved Automatic Modulation Recognition (AMR) tasks. But this success relies heavily on big data. Handling all this data is a problem because it takes up lots of space and is hard to move around. To fix this, scientists have developed ways to shrink the data into smaller versions that still work well. The signals we’re talking about are special because they behave differently in different areas, so we need new ways to understand and process them. A new way to compress data called Multi-domain Distribution Matching (MDM) is being tested. MDM uses a technique called Discrete Fourier Transform (DFT) to change the format of the signals and then compares the real data with smaller versions of it. This helps us make better predictions about what the signals will look like in different situations. |
Keywords
» Artificial intelligence » Deep learning » Distillation
