Summary of Citrus: Squeezing Extra Performance Out Of Low-data Bio-signal Transfer Learning, by Eloy Geenjaar and Lie Lu
CiTrus: Squeezing Extra Performance out of Low-data Bio-signal Transfer Learning
by Eloy Geenjaar, Lie Lu
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A new hybrid model architecture is proposed for low-data bio-signal transfer learning. The approach involves pre-training a neural network on a large dataset with a self-supervised task, replacing the head with a linear classification head, and fine-tuning on different downstream datasets. A frequency-based masked auto-encoding task is introduced, along with a more comprehensive evaluation framework. The model is evaluated on various bio-signal datasets, showing that the convolution-only part achieves state-of-the-art performance on some tasks, while the full model improves performance further. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to improve prediction performance on small bio-signal datasets. Researchers are using a technique called transfer learning, where they train a neural network on a large dataset and then fine-tune it for specific tasks. The team proposes a new type of model that combines convolutional and transformer layers. They also create a special task for the pre-training phase that involves masking certain parts of the data. By comparing different approaches, they find that their method can achieve better results than others in some cases. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Neural network » Self supervised » Transfer learning » Transformer
