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Found 10 papers, 2 papers with code
Test-Time Adaptation with Principal Component Analysis
Machine Learning models are prone to fail when test data are different from training data, a situation often encountered in real applications known as distribution shift.
Towards Clear Expectations for Uncertainty Estimation
If Uncertainty Quantification (UQ) is crucial to achieve trustworthy Machine Learning (ML), most UQ methods suffer from disparate and inconsistent evaluation protocols.
Performance Prediction Under Dataset Shift
ML models deployed in production often have to face unknown domain changes, fundamentally different from their training settings.
Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query Shift
To classify query instances from novel classes encountered at test-time, they only require a support set composed of a few labelled samples.
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.
Target Consistency for Domain Adaptation: when Robustness meets Transferability
Learning Invariant Representations has been successfully applied for reconciling a source and a target domain for Unsupervised Domain Adaptation.
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.
