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Found 3 papers, 1 papers with code
On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization
To enable risk-averse predictions from a DG classifier, we propose a novel inference procedure, Test-Time Neural Style Smoothing (TT-NSS), that uses a "style-smoothed" version of the DG classifier for prediction at test time.
Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupervised Domain Adaptation
The adoption of UDA with multiple sources can simultaneously enrich the training set and bridge the domain gap between different skin lesion datasets, which vary due to distinct acquisition protocols.
Analysis of Task Transferability in Large Pre-trained Classifiers
We propose a novel Task Transfer Analysis approach that transforms the source distribution (and classifier) by changing the class prior distribution, label, and feature spaces to produce a new source distribution (and classifier) and allows us to relate the loss of the downstream task (i. e., transferability) to that of the source task.
