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Few-shot classification on CUB

93.18Accuracy

MCL-Katz

Updated 4d ago

Evaluation Results

Method
MCL-Katz 2021.06	93.18	-	-	-	-	-
FRN 2021.06	92.59	-	-	-	-	-
DN4 2021.06	92.51	-	-	-	-	-
MCL 2021.06	92.18	-	-	-	-	-
ProtoNet + MCL 2021.06	91.71	-	-	-	-	-
DeepEMD 2021.06	91.6	-	-	-	-	-
ProtoNet 2021.06	91.38	-	-	-	-	-
DSN 2021.06	91.19	-	-	-	-	-
CTX 2021.06	90.9	-	-	-	-	-
MCL-Katz 2021.06	90.56	-	-	-	-	-
Supervised 2023.03	89.13	-	-	-	-	-
MCL 2021.06	88.71	-	-	-	-	-
DN4 2021.06	88.43	-	-	-	-	-
FRN 2021.06	88.43	-	-	-	-	-
CTX 2021.06	87.31	-	-	-	-	-
APN+f-VAEGAN-D2 2022.04	87.1	-	-	-	-	-
ProtoNet + MCL 2021.06	86.95	-	-	-	-	-
APN+TF-VAEGAN 2022.04	86.6	-	-	-	-	-
f-VAEGAN-D2 2022.04	85.9	-	-	-	-	-
DeepEMD 2021.06	85.68	-	-	-	-	-
MCL-Katz 2021.06	85.63	-	-	-	-	-
TF-VAEGAN 2022.04	85.6	-	-	-	-	-
DN4 2021.06	85.44	-	-	-	-	-
DSN 2021.06	85.41	-	-	-	-	-
Imprinted 2022.04	85.3	-	-	-	-	-
APN+TF-VAEGAN 2022.04	85.2	-	-	-	-	-
APN+f-VAEGAN-D2 2022.04	84.8	-	-	-	-	-
ProtoNet + MCL 2021.06	84.59	-	-	-	-	-
MCL 2021.06	83.64	-	-	-	-	-
TF-VAEGAN 2022.04	83.5	-	-	-	-	-
f-VAEGAN-D2 2022.04	83.4	-	-	-	-	-
DeepEMD 2021.06	83.35	-	-	-	-	-
FRN 2021.06	83.16	-	-	-	-	-
PsCo 2023.03	83.13	-	-	-	-	-
ProtoNet 2021.06	82.98	-	-	-	-	-
APN+TF-VAEGAN 2022.04	82.6	-	-	-	-	-
C-Hyperbolic 2021.07	81.76	-	-	-	-	-
ProtoNet 2021.06	81.5	-	-	-	-	-
Analogy 2022.04	81.1	-	-	-	-	-
APN+f-VAEGAN-D2 2022.04	81.1	-	-	-	-	-
TF-VAEGAN 2022.04	81.1	-	-	-	-	-
DSN 2021.06	80.8	-	-	-	-	-
Imprinted 2022.04	80	-	-	-	-	-
MCL-Katz 2021.06	79.61	-	-	-	-	-
f-VAEGAN-D2 2022.04	79.6	-	-	-	-	-
Hyperbolic 2021.07	79.51	-	-	-	-	-
CTX 2021.06	78.47	-	-	-	-	-
DN4 2021.06	78.31	-	-	-	-	-
Analogy 2022.04	78	-	-	-	-	-
MCL 2021.06	77.8	-	-	-	-	-
APN+f-VAEGAN-D2 2022.04	77.8	-	-	-	-	-
APN+TF-VAEGAN 2022.04	77.1	-	-	-	-	-
ProtoNet + MCL 2021.06	76.87	-	-	-	-	-
PsCo 2023.03	76.63	-	-	-	-	-
f-VAEGAN-D2 2022.04	76.1	-	-	-	-	-
TF-VAEGAN 2022.04	75.6	-	-	-	-	-
DeepEMD 2021.06	75.34	-	-	-	-	-
FRN 2021.06	73.48	-	-	-	-	-
ProtoNet 2021.06	71.64	-	-	-	-	-
Euclidean 2021.07	70.77	-	-	-	-	-
PsCo 2023.03	70.08	-	-	-	-	-
CTX 2021.06	69.64	-	-	-	-	-
Analogy 2022.04	69.5	-	-	-	-	-
PsCo 2023.03	68.58	-	-	-	-	-
BYOL 2023.03	67.45	-	-	-	-	-
GNN (FT) 2020.01	66.98	-	-	-	-	-
DSN 2021.06	66.01	-	-	-	-	-
ProtoNets 2023.03	65.03	-	-	-	-	-
Imprinted 2022.04	65	-	-	-	-	-
C-Hyperbolic 2021.07	64.66	-	-	-	-	-
MAML 2023.03	64.17	-	-	-	-	-
MoCo v2 2023.03	64.16	-	-	-	-	-
MoCo v2 2023.03	62.81	-	-	-	-	-
GNN 2020.01	62.25	-	-	-	-	-
SimCLR 2023.03	61.89	-	-	-	-	-
MoCo v2 2023.03	61.88	-	-	-	-	-
SWAV 2023.03	61.38	-	-	-	-	-
Hyperbolic 2021.07	61.18	-	-	-	-	-
RelationNet (FT) 2020.01	59.46	-	-	-	-	-
RelationNet 2020.01	57.77	-	-	-	-	-
PsCo 2023.03	57.38	-	-	-	-	-
ProtoNets 2023.03	56.74	-	-	-	-	-
MAML 2023.03	56.57	-	-	-	-	-
Analogy 2022.04	56.5	-	-	-	-	-
MatchingNet (FT) 2020.01	55.23	-	-	-	-	-
MetaOptNet-SVM-trainval 2020.01	54.67	-	-	-	-	-
Meta-SVEBM 2023.03	54.61	-	-	-	-	-
Meta-GMVAE 2023.03	54.08	-	-	-	-	-
MoCo v2 2023.03	53.23	-	-	-	-	-
SimCLR 2023.03	52.11	-	-	-	-	-
SWAV 2023.03	51.58	-	-	-	-	-
MatchingNet 2020.01	51.37	-	-	-	-	-
Euclidean 2021.07	51.31	-	-	-	-	-
Imprinted 2022.04	48.5	-	-	-	-	-
Meta-GMVAE 2023.03	47.48	-	-	-	-	-
Meta-SVEBM 2023.03	45.5	-	-	-	-	-
MatchingNet 2020.06	-	73.49	84.45	-	-	-
MAML 2020.06	-	68.42	83.47	-	-	-
ProtoNet 2020.06	-	72.99	86.64	-	-	-
RelationNet 2020.06	-	68.58	84.05	-	-	-

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