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ResearchFormerly WizWand

Active Learning Image Classification on CheXpert 1.0 (test)

83.23AccuracyTD-FT (Ours)
64.894869.654974.41579.1751Oct 29, 2025
Updated 8mo ago

Evaluation results

MethodLinks
TD-FT (Ours)Acquisition Strategy=Power batch acquisition, Representation Learning Method=Task-Driven Fine-Tuning, Encoder Backbone=ResNet182025.1083.23
TD-FT RandomAcquisition Strategy=Random, Representation Learning Method=Task-Driven Fine-Tuning, Encoder Backbone=ResNet182025.1081.67
GALAXYAcquisition Strategy=GALAXY, Representation Learning Method=Original Baseline, Encoder Backbone=ResNet182025.1078.76
US+EPIG (SimCLRv2)Acquisition Strategy=EPIG, Representation Learning Method=SimCLRv2, Encoder Backbone=ResNet182025.1077.84
TD-SPLIT (Ours)Acquisition Strategy=Power batch acquisition, Representation Learning Method=Task-Driven SPLIT, Encoder Backbone=ResNet182025.1076.47
TD-SPLIT RandomAcquisition Strategy=Random, Representation Learning Method=Task-Driven SPLIT, Encoder Backbone=ResNet182025.1075.79
Cluster MarginAcquisition Strategy=Cluster Margin, Representation Learning Method=Original Baseline, Encoder Backbone=ResNet182025.1075.41
US Random (SimCLRv2)Acquisition Strategy=Random, Representation Learning Method=SimCLRv2, Encoder Backbone=ResNet182025.1074.7
SIMILARAcquisition Strategy=SIMILAR, Representation Learning Method=Original Baseline, Encoder Backbone=ResNet182025.1071.94
US+EPIG (VAE)Acquisition Strategy=EPIG, Representation Learning Method=VAE, Encoder Backbone=Burgess encoder2025.1066.69
US Random (VAE)Acquisition Strategy=Random, Representation Learning Method=VAE, Encoder Backbone=Burgess encoder2025.1065.6