Few Shot Image Classification On Meta Dataset

Accuracy

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

比較表

モデル名	Accuracy
improving-few-shot-visual-classification-with	70.32
prototypical-networks-for-few-shot-learning	60.573
matching-networks-for-one-shot-learning	56.247
improved-few-shot-visual-classification	69.86
task-specific-preconditioner-for-cross-domain	81.40
shallow-bayesian-meta-learning-for-real-world	74.3
meta-dataset-a-dataset-of-datasets-for	63.428
contextual-squeeze-and-excitation-for	76.1
improving-task-adaptation-for-cross-domain	78.07
meta-dataset-a-dataset-of-datasets-for	54.319
selecting-relevant-features-from-a-universal	70.72
selecting-relevant-features-from-a-universal	69.3
universal-representation-learning-from	75.75
exploring-complementary-strengths-of	68.89
meta-dataset-a-dataset-of-datasets-for	58.758
contextual-squeeze-and-excitation-for	74.9
model-agnostic-meta-learning-for-fast	57.024
a-universal-representation-transformer-layer	72.15
fast-and-flexible-multi-task-classification	66.9
pushing-the-limits-of-simple-pipelines-for	84.75
unleashing-the-power-of-meta-tuning-for-few	85.27
learning-to-compare-relation-network-for-few	53.315