Wang, HongyuFraser, HuonGouk, HenryFrank, EibePfahringer, BernhardMayo, MichaelHolmes, GeoffreyBrazdil, Pvan Rijn, JNGouk, HMohr, F2023-01-252023-01-252022https://hdl.handle.net/10289/15453We summarise experiments (Wang et al., 2022) evaluating cross-domain few-shot learning (CDFSL) with feature extractors trained on ImageNet. The work explores the transfer performance of extracted features on five target domains with different degrees of similarity to ImageNet. These experiments compare robust classifiers and normalisation methods, consider multi-instance learning algorithms, and evaluate the effect of using features extracted by different ResNet backbones at various levels of their convolutional hierarchies. The cosine similarity classifier and 1-vs-rest logistic regression with ℓ2 regularisation are the top-performing robust classifiers in the evaluation, and ℓ2 normalisation improves performance on all five target domains when using LDA as the robust classifier. The results also show that feature extractors with the highest capacity do not always achieve the best CDFSL performance. Lastly, simple multi-instance learning methods are shown to improve classifier accuracy.application/pdfen© 2022 The Authorshttps://proceedings.mlr.press/computer sciencecross-domain few-shot learningnormalisationpretrained feature extractorsConference Contribution