Active Learning Image Classification on F-MNIST (test)
99.56AccuracyTD-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 | 99.56 | |
| US+EPIG (SimCLRv2)Acquisition Strategy=EPIG, Representation Learning Method=SimCLRv2, Encoder Backbone=ResNet182025.10 | 98.53 | |
| TD-SPLIT (Ours)Acquisition Strategy=Power batch acquisition, Representation Learning Method=Task-Driven SPLIT, Encoder Backbone=ResNet182025.10 | 98.46 | |
| TD-FT RandomAcquisition Strategy=Random, Representation Learning Method=Task-Driven Fine-Tuning, Encoder Backbone=ResNet182025.10 | 96.23 | |
| US+EPIG (VAE)Acquisition Strategy=EPIG, Representation Learning Method=VAE, Encoder Backbone=Burgess encoder2025.10 | 94.5 | |
| Cluster MarginAcquisition Strategy=Cluster Margin, Representation Learning Method=Original Baseline, Encoder Backbone=ResNet182025.10 | 94.24 | |
| SIMILARAcquisition Strategy=SIMILAR, Representation Learning Method=Original Baseline, Encoder Backbone=ResNet182025.10 | 93.82 | |
| US Random (SimCLRv2)Acquisition Strategy=Random, Representation Learning Method=SimCLRv2, Encoder Backbone=ResNet182025.10 | 92.76 | |
| TD-SPLIT RandomAcquisition Strategy=Random, Representation Learning Method=Task-Driven SPLIT, Encoder Backbone=ResNet182025.10 | 88.19 | |
| US Random (VAE)Acquisition Strategy=Random, Representation Learning Method=VAE, Encoder Backbone=Burgess encoder2025.10 | 86.5 | |
| GALAXYAcquisition Strategy=GALAXY, Representation Learning Method=Original Baseline, Encoder Backbone=ResNet182025.10 | 84.74 |