Continual Learning On Stanford Cars Fine
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Model Name | Accuracy | Paper Title | Repository |
|---|---|---|---|
| H$^{2}$ | 90.6 | Helpful or Harmful: Inter-Task Association in Continual Learning | |
| PackNet | 86.11 | PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning | |
| ProgressiveNet | 89.21 | Progressive Neural Networks | |
| Piggyback | 89.62 | Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights | |
| CondConvContinual | 92.61 | EXTENDING CONDITIONAL CONVOLUTION STRUCTURES FOR ENHANCING MULTITASKING CONTINUAL LEARNING | |
| CPG | 92.80 | Compacting, Picking and Growing for Unforgetting Continual Learning |
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