Vanilla transformers on the emg2qwerty dataset improve cross-user typing accuracy up to 109M parameters, and simple logit distillation recovers most of the gain in a 2.2M-parameter student.
Deep learning with convolutional neural networks applied to electromyography data: A resource for the classification of movements for prosthetic hands
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Scaling and Distilling Transformer Models for sEMG
Vanilla transformers on the emg2qwerty dataset improve cross-user typing accuracy up to 109M parameters, and simple logit distillation recovers most of the gain in a 2.2M-parameter student.