Few Shot Image Classification On Tiered 1
评估指标
Accuracy
评测结果
各个模型在此基准测试上的表现结果
比较表格
| 模型名称 | Accuracy |
|---|---|
| self-supervised-knowledge-distillation-for | 86.66 |
| exploring-complementary-strengths-of | 87.08 |
| hypertransformer-model-generation-for | 73.9% |
| metafun-meta-learning-with-iterative | 83.28 |
| improving-few-shot-visual-classification-with | 81.8 |
| unsupervised-embedding-adaptation-via-early | 87.61 |
| easy-ensemble-augmented-shot-y-shaped | 88.33 |
| easy-ensemble-augmented-shot-y-shaped | 89.76 |
| instance-credibility-inference-for-few-shot | 89.00 |
| geometric-mean-improves-loss-for-few-shot | 84.04 |
| context-aware-meta-learning | 98.8 |
| adaptive-cross-modal-few-shot-learning | 82.58 |
| pseudo-shots-few-shot-learning-with-auxiliary | 86.82 |
| transductive-information-maximization-for-few | 89.8 |
| match-them-up-visually-explainable-few-shot | 77.82 |
| adaptive-dimension-reduction-and-variational | 90.41 |
| 190600562 | 85.2 |
| self-supervision-can-be-a-good-few-shot | 86.51 |
| shallow-bayesian-meta-learning-for-real-world | 89.56 |
| diffkendall-a-novel-approach-for-few-shot | 85.31 |
| meta-learning-with-differentiable-convex | 81.75 |
| improving-few-shot-visual-classification-with | 87.7 |
| espt-a-self-supervised-episodic-spatial | 87.49 |
| uncertainty-in-model-agnostic-meta-learning | 82.7 |
| attribute-surrogates-learning-and-spectral | 91.72 |
| meta-learning-with-a-geometry-adaptive | 74.41 |
| rethinking-generalization-in-few-shot-1 | 89.96 |
| improved-few-shot-visual-classification | 86.0 |
| bridging-multi-task-learning-and-meta | 83.69 |
| few-shot-learning-as-domain-adaptation | 85.82 |
| match-them-up-visually-explainable-few-shot | 80.05 |
| easy-ensemble-augmented-shot-y-shaped | 89.26 |
| adaptive-subspaces-for-few-shot-learning | 83.32 |
| laplacian-regularized-few-shot-learning | 87.93 |
| open-set-likelihood-maximization-for-few-shot | 86.35 |
| deep-comparison-relation-columns-for-few-shot | 79.62 |
| unsupervised-embedding-adaptation-via-early | 86.34 |
| meta-learning-with-a-geometry-adaptive | 74.9 |
| squeezing-backbone-feature-distributions-to | 91.09 |
| sparse-spatial-transformers-for-few-shot | 86.61 |
| leveraging-the-feature-distribution-in | 90.44 |
| improved-few-shot-visual-classification | 80.0 |
| hypertransformer-model-generation-for | 73.2% |
| charting-the-right-manifold-manifold-mixup | 88.59 |
| fast-and-generalized-adaptation-for-few-shot | 86.75 |
| relational-embedding-for-few-shot | 85.28 |
| embedding-propagation-smoother-manifold-for | 88.36 |
| pac-bayesian-meta-learning-with-implicit | 81.84 |
| easy-ensemble-augmented-shot-y-shaped | 87.86 |
| tapnet-neural-network-augmented-with-task | 80.26 |
| transductive-decoupled-variational-inference | 96.57 |