Few Shot Image Classification On Cifar Fs 5 1

Metriken

Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Vergleichstabelle

Modellname	Accuracy
easy-ensemble-augmented-shot-y-shaped	90.2
the-self-optimal-transport-feature-transform	92.83
adaptive-subspaces-for-few-shot-learning	87.3
adaptive-dimension-reduction-and-variational	90.63
bridging-multi-task-learning-and-meta	84.1
region-comparison-network-for-interpretable	77.63
match-them-up-visually-explainable-few-shot	80.16
pushing-the-limits-of-simple-pipelines-for	92.2
attribute-surrogates-learning-and-spectral	90.50
shallow-bayesian-meta-learning-for-real-world	88.79
exploring-complementary-strengths-of	89.74
rethinking-generalization-in-few-shot-1	88.90
context-aware-meta-learning	93.5
meta-learning-with-differentiable-convex	85
easy-ensemble-augmented-shot-y-shaped	90.47
instance-credibility-inference-for-few-shot	84.32
transfer-learning-based-few-shot	90.73
easy-ensemble-augmented-shot-y-shaped	89.0
match-them-up-visually-explainable-few-shot	82.93
charting-the-right-manifold-manifold-mixup	87.47
sparse-spatial-transformers-for-few-shot	86.61
task-augmentation-by-rotating-for-meta	88.38
sill-net-feature-augmentation-with-separated	91.09
constellation-nets-for-few-shot-learning	86.8
relational-embedding-for-few-shot	86.60
self-supervised-knowledge-distillation-for	88.9
task-augmentation-by-rotating-for-meta	88.33
the-balanced-pairwise-affinities-feature	92.83
learning-to-compare-relation-network-for-few	69.3
easy-ensemble-augmented-shot-y-shaped	88.38
region-comparison-network-for-interpretable	82.96
complementing-representation-deficiency-in	85.2
empirical-bayes-transductive-meta-learning-1	85.3
fast-and-generalized-adaptation-for-few-shot	89.3
pseudo-shots-few-shot-learning-with-auxiliary	89.12
geometric-mean-improves-loss-for-few-shot	85.08
squeezing-backbone-feature-distributions-to	91.86
leveraging-the-feature-distribution-in	90.68
complementing-representation-deficiency-in	86.8