15 lines
1.1 KiB
Markdown
15 lines
1.1 KiB
Markdown
This document is about my ideas what we can do, based on what I've read in papers or anywhere on the internet.
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## Augmentation
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### Use GAN
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Gan generates new samples of data. Generator is trained alongside descriminator. After that we have a generator capable of generating new data. I mean it is not augmentation of dataset, it is creating whole new dataset.
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Tho the generator needs some input. And that is the question, what should it be.
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* One option is to use labels with some random noise as input.
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* This paper shows different approach [[Augmentation methods#Generative Adversarial Networks-Based Data Augmentation for Brain–Computer Interface(2020)]]. Based on that I propose method where we would extract something like **movement-specific feature vector** which would together with noise be input to generator.
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### Contrastive learning
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youtube video with explanation: https://www.youtube.com/watch?v=UqJauYELn6c
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## Feature Extraction
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I think we should try variational autoendcoders, and some novel architecture, like VQVAE(vector quantization should bring narrower space for classifier at the end). |