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Awesome Test-Time Prior Adaptation Awesome

A curated list of awesome test-time prior adaptation resources. Your contributions are always welcome!

Contents

Test Time Prior Adaptation

  • CM-Bootstrap [Vucetic, Obradovic, Proc. ECML 2001] Classification on data with biased class distribution [PDF] [G-Scholar]

  • MLLS [Latinne et al., Proc. ICML 2001] Adjusting the outputs of a classifier to new a priori probabilities may significantly improve classification accuracy: evidence from a multi-class problem in remote sensing [PDF] [G-Scholar]

  • MLLS [Saerens et al., Neural Computation 2002] Adjusting the outputs of a classifier to new a priori probabilities: A simple procedure [PDF] [G-Scholar]

  • ... [Chan et al., Proc. ACL 2006] Estimating class priors in domain adaptation for word sense disambiguation [PDF] [G-Scholar]

  • OEM [Yang and Zhou, Pattern Recognition 2008] Non-stationary data sequence classification using online class priors estimation [PDF] [G-Scholar]

  • MLLS-PE [Du Plessis and Sugiyama, Neural Networks 2014] Semi-supervised learning of class balance under class-prior change by distribution matching [PDF] [G-Scholar]

  • PTCA [Royer and Lampert, Proc. CVPR 2015] Classifier adaptation at prediction time [PDF] [G-Scholar]

  • BBSE [Lipton et al., Proc. ICML 2018] Detecting and correcting for label shift with black box predictors [PDF] [G-Scholar] [CODE]

  • RLLS [Azizzadenesheli et al., Proc. ICLR 2019] Regularized learning for domain adaptation under label shifts [PDF] [G-Scholar] [CODE]

  • ... [Sulc and Matas, Proc. ICCV Workshops 2019] Improving CNN classifiers by estimating test-time priors [PDF] [G-Scholar]

  • BCTS [Alexandari et al., Proc. ICML 2020] Maximum likelihood with bias-corrected calibration is hard-to-beat at label shift adaptation [PDF] [G-Scholar] [CODE]

  • MLLS-CM [Garg et al., Proc. NeurIPS 2020] A unified view of label shift estimation [PDF] [G-Scholar]

  • ... [Sulc et al., Proc. WACV 2020] Fungi recognition: A practical use case [PDF] [G-Scholar] [CODE]

  • ... [Šipka, Thesis 2021] Adaptation of CNN classifiers to prior shift [PDF] [G-Scholar--]

  • OGD [Wu et al., Proc. NeurIPS 2021] Online adaptation to label distribution shift [PDF] [G-Scholar] [CODE]

  • ... [Šipka et al., Proc. WACV 2022] The hitchhiker's guide to prior-shift adaptation [PDF] [G-Scholar]

  • TTLSA [Sun et al., Arxiv 2022] Beyond invariance: Test-time label-shift adaptation for distributions with" spurious" correlations [PDF] [G-Scholar] [CODE]

  • SADE [Zhang et al., Proc. NeurIPS 2022] Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition [PDF] [G-Scholar] [CODE]

  • TTADC [Ma et al., Proc. MICCAI 2022] Test-time adaptation with calibration of medical image classification nets for label distribution shift [PDF] [G-Scholar] [CODE]