Summary of Sim2real in Reconstructive Spectroscopy: Deep Learning with Augmented Device-informed Data Simulation, by Jiyi Chen et al.
Sim2Real in Reconstructive Spectroscopy: Deep Learning with Augmented Device-Informed Data Simulation
by Jiyi Chen, Pengyu Li, Yutong Wang, Pei-Cheng Ku, Qing Qu
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 Sim2Real framework uses deep learning to efficiently reconstruct spectral signals in reconstructive spectroscopy. The challenge is to use simulated data, which are easier to collect but exhibit significant distribution shifts from real-world data. To address this, a hierarchical data augmentation strategy is introduced to mitigate the domain shift. A corresponding neural network is designed for spectral signal reconstruction using augmented data. Experiments demonstrate that Sim2Real achieves speed-up during inference while matching state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sim2Real is a new way to reconstruct real-world signals using only simulated data. This helps make it easier and faster to get accurate results. The problem is that the simulated data don’t look exactly like the real-world data, which makes it hard to use them. To fix this, scientists created a special way to change the simulated data to make them more similar to the real-world data. They then designed a new computer program to reconstruct the signals using this special data. The results show that Sim2Real is really fast and gets good results. |
Keywords
* Artificial intelligence * Data augmentation * Deep learning * Inference * Neural network
