Few Shot Image Classification On Fc100 5 Way 1

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Tableau comparatif

Nom du modèle	Accuracy
fast-and-generalized-adaptation-for-few-shot	66.9
bridging-multi-task-learning-and-meta	57.7
meta-transfer-learning-for-few-shot-learning	57.6
exploring-complementary-strengths-of	65.3
sparse-spatial-transformers-for-few-shot	58.92
tadam-task-dependent-adaptive-metric-for	56.1
easy-ensemble-augmented-shot-y-shaped	65.82
adaptive-dimension-reduction-and-variational	70.60
self-supervised-knowledge-distillation-for	63.1
easy-ensemble-augmented-shot-y-shaped	64.74
task-augmentation-by-rotating-for-meta	67.17
attribute-surrogates-learning-and-spectral	66.42
meta-learning-with-differentiable-convex	62.5
constellation-nets-for-few-shot-learning	59.7
rethinking-generalization-in-few-shot-1	63.81
enhancing-few-shot-image-classification	66.27
pseudo-shots-few-shot-learning-with-auxiliary	61.58
easy-ensemble-augmented-shot-y-shaped	64.14
task-augmentation-by-rotating-for-meta	67.66
complementing-representation-deficiency-in	57.8
complementing-representation-deficiency-in	56.6
easy-ensemble-augmented-shot-y-shaped	66.86