Active Learning Image Classification on CheXpert 1.0 (test)
83.23AccuracyTD-FT (Ours)
Evaluation Results
| Method | Links | |
|---|---|---|
| TD-FT (Ours)Acquisition Strategy=Power batch acquisition, Representation Learning Method=Task-Driven Fine-Tuning, Encoder Backbone=ResNet182025.10 | 83.23 | |
| TD-FT RandomAcquisition Strategy=Random, Representation Learning Method=Task-Driven Fine-Tuning, Encoder Backbone=ResNet182025.10 | 81.67 | |
| GALAXYAcquisition Strategy=GALAXY, Representation Learning Method=Original Baseline, Encoder Backbone=ResNet182025.10 | 78.76 | |
| US+EPIG (SimCLRv2)Acquisition Strategy=EPIG, Representation Learning Method=SimCLRv2, Encoder Backbone=ResNet182025.10 | 77.84 | |
| TD-SPLIT (Ours)Acquisition Strategy=Power batch acquisition, Representation Learning Method=Task-Driven SPLIT, Encoder Backbone=ResNet182025.10 | 76.47 | |
| TD-SPLIT RandomAcquisition Strategy=Random, Representation Learning Method=Task-Driven SPLIT, Encoder Backbone=ResNet182025.10 | 75.79 | |
| Cluster MarginAcquisition Strategy=Cluster Margin, Representation Learning Method=Original Baseline, Encoder Backbone=ResNet182025.10 | 75.41 | |
| US Random (SimCLRv2)Acquisition Strategy=Random, Representation Learning Method=SimCLRv2, Encoder Backbone=ResNet182025.10 | 74.7 | |
| SIMILARAcquisition Strategy=SIMILAR, Representation Learning Method=Original Baseline, Encoder Backbone=ResNet182025.10 | 71.94 | |
| US+EPIG (VAE)Acquisition Strategy=EPIG, Representation Learning Method=VAE, Encoder Backbone=Burgess encoder2025.10 | 66.69 | |
| US Random (VAE)Acquisition Strategy=Random, Representation Learning Method=VAE, Encoder Backbone=Burgess encoder2025.10 | 65.6 |