Few Shot Image Classification On Stanford 3
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Model Name | Accuracy | Paper Title | Repository |
|---|---|---|---|
| Matching Nets FCE++ | 44.70 | Matching Networks for One Shot Learning | |
| GNN++ | 71.25 | Few-Shot Learning with Graph Neural Networks | |
| MML(KL) | 91.05 | Multi-level Metric Learning for Few-shot Image Recognition | - |
| MATANet | 91.89 | Multi-scale Adaptive Task Attention Network for Few-Shot Learning | - |
| Prototypical Nets++ | 52.93 | Prototypical Networks for Few-shot Learning | |
| DN4-DA (k=1) | 89.6 | Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning |
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