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Stochastic Adversarial Gradient Embedding for Active Domain Adaptation
First, we select for annotation target samples that are likely to improve the representations' transferability by measuring the variation, before and after annotation, of the transferability loss gradient.
Robust Domain Adaptation: Representations, Weights and Inductive Bias
The emergence of Domain Invariant Representations (IR) has improved drastically the transferability of representations from a labelled source domain to a new and unlabelled target domain.
Domain-Invariant Representations: A Look on Compression and Weights
Second, we show that learning weighted representations plays a key role in relaxing the constraint of invariance and then preserving the risk of compression.
Learning Invariant Representations for Sentiment Analysis: The Missing Material is Datasets
Learning representations which remain invariant to a nuisance factor has a great interest in Domain Adaptation, Transfer Learning, and Fair Machine Learning.
Hidden Covariate Shift: A Minimal Assumption For Domain Adaptation
Such approach consists in learning a representation of the data such that the label distribution conditioned on this representation is domain invariant.
