Active Learning Image Classification on CIFAR-10+100 1.0 (test)
80.9AccuracyTD-FT (Ours)
Evaluation Results
| Method | Links | |
|---|---|---|
| TD-FT (Ours)Acquisition Strategy=Power batch acquisition, Representation Learning Method=Task-Driven Fine-Tuning, Encoder Backbone=ResNet502025.10 | 80.9 | |
| TD-FT RandomAcquisition Strategy=Random, Representation Learning Method=Task-Driven Fine-Tuning, Encoder Backbone=ResNet502025.10 | 77.14 | |
| US+EPIG (SimCLRv2)Acquisition Strategy=EPIG, Representation Learning Method=SimCLRv2, Encoder Backbone=ResNet502025.10 | 76.19 | |
| US Random (SimCLRv2)Acquisition Strategy=Random, Representation Learning Method=SimCLRv2, Encoder Backbone=ResNet502025.10 | 74.6 | |
| TD-SPLIT (Ours)Acquisition Strategy=Power batch acquisition, Representation Learning Method=Task-Driven SPLIT, Encoder Backbone=ResNet502025.10 | 59.84 | |
| GALAXYAcquisition Strategy=GALAXY, Representation Learning Method=Original Baseline, Encoder Backbone=ResNet502025.10 | 55.28 | |
| TD-SPLIT RandomAcquisition Strategy=Random, Representation Learning Method=Task-Driven SPLIT, Encoder Backbone=ResNet502025.10 | 54.9 | |
| Cluster MarginAcquisition Strategy=Cluster Margin, Representation Learning Method=Original Baseline, Encoder Backbone=ResNet502025.10 | 32.79 | |
| SIMILARAcquisition Strategy=SIMILAR, Representation Learning Method=Original Baseline, Encoder Backbone=ResNet502025.10 | 30.87 | |
| US+EPIG (VAE)Acquisition Strategy=EPIG, Representation Learning Method=VAE, Encoder Backbone=ResNet-VAE2025.10 | 30.47 | |
| US Random (VAE)Acquisition Strategy=Random, Representation Learning Method=VAE, Encoder Backbone=ResNet-VAE2025.10 | 27.9 |