Few Shot Image Classification On Cifar Fs 5

평가 지표

Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

비교 표

모델 이름	Accuracy
complementing-representation-deficiency-in	73.8
task-augmentation-by-rotating-for-meta	76.75
sill-net-feature-augmentation-with-separated	87.73
the-self-optimal-transport-feature-transform	89.94
shallow-bayesian-meta-learning-for-real-world	75.83
adaptive-dimension-reduction-and-variational	87.35
match-them-up-visually-explainable-few-shot	66.31
empirical-bayes-transductive-meta-learning-1	80.0
constellation-nets-for-few-shot-learning	75.4
fast-and-generalized-adaptation-for-few-shot	73.1
exploring-complementary-strengths-of	77.87
relational-embedding-for-few-shot	74.51
instance-credibility-inference-for-few-shot	76.51
geometric-mean-improves-loss-for-few-shot	71.09
easy-ensemble-augmented-shot-y-shaped	86.99
easy-ensemble-augmented-shot-y-shaped	87.16
pseudo-shots-few-shot-learning-with-auxiliary	81.87
adaptive-subspaces-for-few-shot-learning	78
easy-ensemble-augmented-shot-y-shaped	75.24
the-balanced-pairwise-affinities-feature	89.94
easy-ensemble-augmented-shot-y-shaped	76.2
rethinking-generalization-in-few-shot-1	77.76
region-comparison-network-for-interpretable	69.02
charting-the-right-manifold-manifold-mixup	74.81
bridging-multi-task-learning-and-meta	69.5
meta-learning-with-differentiable-convex	72.8
match-them-up-visually-explainable-few-shot	68.34
sparse-spatial-transformers-for-few-shot	74.5
transfer-learning-based-few-shot	87.79
context-aware-meta-learning	83.3
self-supervised-knowledge-distillation-for	76.9
attribute-surrogates-learning-and-spectral	78.89
region-comparison-network-for-interpretable	61.61
task-augmentation-by-rotating-for-meta	77.66
complementing-representation-deficiency-in	74.7
leveraging-the-feature-distribution-in	87.69
pushing-the-limits-of-simple-pipelines-for	84.3
squeezing-backbone-feature-distributions-to	88.44