Publication: Invariance versus Adversarial Learning in Domain Generalization with Applications to Neuroscience
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2022-05-23
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Tu, Eddie Yuchen. 2022. Invariance versus Adversarial Learning in Domain Generalization with Applications to Neuroscience. Bachelor's thesis, Harvard College.
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Abstract
We explore the practical application of two modern domain
invariant representation-learning techniques for addressing the domain
generalization problem in statistical machine learning. Specifically, we
investigate the performance of cutting-edge adversarial neural
networks and invariance-based invariant risk minimization for
producing representations in a real neuroscience data setting. The
mappings that are learned at training time are then utilized at test
time to produce representations on unlabeled covariates of a held-out
distribution shifted domain for a binary classification problem.
The results of these experiments are useful in practical contexts
including medical diagnosis, object recognition, segmentation, and
action recognition as models created/trained on data in these fields
suffer from issues of data exchangeability. We typically see this occur
when the underlying distributions of collected data tend to vary
across geographical location or methodology from study to study. To
our knowledge, our results provide first insights into empirical results
of purely theoretical modern solutions to this domain generalization
problem in the adversarial and invariance learning spaces.
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Keywords
Adversarial Learning, Covariate Shift, Domain Generalization, Invariance Risk Minimization, Computer science, Statistics
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