Few Shot Image Classification On Tiered
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Accuracy |
|---|---|
| tapnet-neural-network-augmented-with-task | 63.08 |
| diffkendall-a-novel-approach-for-few-shot | 70.76 |
| context-aware-meta-learning | 96.8 |
| attribute-surrogates-learning-and-spectral | 79.67 |
| adaptive-subspaces-for-few-shot-learning | 68.44 |
| 190600562 | 77.7 |
| unsupervised-embedding-adaptation-via-early | 80.13 |
| relational-embedding-for-few-shot | 71.61 |
| easy-ensemble-augmented-shot-y-shaped | 83.98 |
| match-them-up-visually-explainable-few-shot | 62.42 |
| transductive-information-maximization-for-few | 82.1 |
| easy-ensemble-augmented-shot-y-shaped | 74.71 |
| geometric-mean-improves-loss-for-few-shot | 69.61 |
| easy-ensemble-augmented-shot-y-shaped | 74.31 |
| pac-bayesian-meta-learning-with-implicit | 70.82 |
| deep-comparison-relation-columns-for-few-shot | 68.83 |
| adaptive-cross-modal-few-shot-learning | 69.08 |
| improving-few-shot-visual-classification-with | 65.9 |
| improved-few-shot-visual-classification | 71.4 |
| adaptive-dimension-reduction-and-variational | 85.20 |
| squeezing-backbone-feature-distributions-to | 86.07 |
| meta-learning-with-differentiable-convex | 65.81 |
| metafun-meta-learning-with-iterative | 67.72 |
| few-shot-learning-as-domain-adaptation | 69.14 |
| uncertainty-in-model-agnostic-meta-learning | 69.87 |
| espt-a-self-supervised-episodic-spatial | 72.68 |
| rethinking-generalization-in-few-shot-1 | 76.32 |
| sparse-spatial-transformers-for-few-shot | 72.52 |
| charting-the-right-manifold-manifold-mixup | 73.71 |
| pseudo-shots-few-shot-learning-with-auxiliary | 76.55 |
| match-them-up-visually-explainable-few-shot | 61.27 |
| bridging-multi-task-learning-and-meta | 67.11 |
| laplacian-regularized-few-shot-learning | 80.30 |
| meta-learning-with-a-geometry-adaptive | 57.6 |
| leveraging-the-feature-distribution-in | 85.41 |
| meta-learning-with-a-geometry-adaptive | 56.86 |
| exploring-complementary-strengths-of | 72.21 |
| transductive-decoupled-variational-inference | 86.97 |
| shallow-bayesian-meta-learning-for-real-world | 74.33 |
| improving-few-shot-visual-classification-with | 73.8 |
| self-supervised-knowledge-distillation-for | 72.03 |
| instance-credibility-inference-for-few-shot | 84.01 |
| fast-and-generalized-adaptation-for-few-shot | 68.77 |
| unsupervised-embedding-adaptation-via-early | 81.77 |
| self-supervision-can-be-a-good-few-shot | 69.60 |
| easy-ensemble-augmented-shot-y-shaped | 84.29 |
| embedding-propagation-smoother-manifold-for | 78.50 |
| open-set-likelihood-maximization-for-few-shot | 76.64 |
| improved-few-shot-visual-classification | 63.0 |