AutoTransfer: Subject Transfer Learning with Censored Representations on Biosignals Data

We provide a regularization framework for subject transfer learning in which we seek to train an encoder and classifier to minimize classification loss, subject to a penalty measuring independence between the latent representation and the subject label. We introduce three notions of independence and corresponding penalty terms using mutual information or divergence as a proxy for independence. For each penalty term, we provide several concrete estimation algorithms, using analytic methods as well as neural critic functions. We provide a hands-off strategy for applying this diverse family of regularization algorithms to a new dataset, which we call "AutoTransfer". We evaluate the performance of these individual regularization strategies and our AutoTransfer method on EEG, EMG, and ECoG datasets, showing that these approaches can improve subject transfer learning for challenging real-world datasets. In this work, we investigate methods for transfer learning in the classification of biosignals data. Previous work has established the difficulty of transfer learning for biosignals and even the issue of so-called "negative transfer", in which naive attempts to combine datasets from multiple subjects or sessions can paradoxically decrease model performance, due to differences in response statistics [1, 2]. We address the problem of subject transfer by training models to be invariant to changes in a nuisance variable representing subject identifier.