Conference paper
Weight Sharing and Deep Learning for Spectral Data
We propose a novel method to co-train deep convolutional neural networks for data sets of differing position specific data. This is an advantage in chemometrics where individual measurements represent exact chemical compounds, e.g. for given wavelengths, and thus signals cannot be translated or resized without disturbing their interpretation.
Our approach outperforms transfer learning for three small data sets co-trained with a medium sized data set.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2020 |
| Pages: | 4227-4231 |
| Proceedings: | 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing |
| Series: | Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing - Proceedings |
| ISBN: | 1509066314 , 1509066322 , 9781509066315 and 9781509066322 |
| ISSN: | 2379190x and 15206149 |
| Types: | Conference paper |
| DOI: | 10.1109/ICASSP40776.2020.9053918 |
| ORCIDs: | Clemmensen, Line |
Conferences Convolutional neural networks Deep learning Signal processing Speech processing Transfer learning Wavelength measurement chemical compounds chemometrics convolutional neural nets data handling deep convolutional neural networks deep learning learning (artificial intelligence) spectral data weight sharing
