Few Shot Image Classification On Mini 6
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Model Name | Accuracy | Paper Title | Repository |
|---|---|---|---|
| DKT + BNCosSim | 56.40 | Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels | |
| LaplacianShot | 66.33 | Laplacian Regularized Few-Shot Learning | |
| Baseline++ (Chen et al., 2019) | 62.04 | A Closer Look at Few-shot Classification | |
| DAPNA | 68.33 | Few-Shot Learning as Domain Adaptation: Algorithm and Analysis | - |
| PT+MAP | 76.51 | Leveraging the Feature Distribution in Transfer-based Few-Shot Learning | |
| TRIDENT | 80.74 | Transductive Decoupled Variational Inference for Few-Shot Classification | |
| PEMnE-BMS | 79.15 | Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning | |
| HyperShot | 58.86 | HyperShot: Few-Shot Learning by Kernel HyperNetworks |
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