Few-shot Domain Generalization on Meta-Dataset ImageNet-only v1 (train) benchmark leaderboard · SOTA2 Research

Few-shot Domain Generalization on Meta-Dataset ImageNet-only v1 (train)

56.66Accuracy (ImageNet)GAP + Proto-MAML

Evaluation Results

Method	Links
GAP + Proto-MAML2023.04		56.66	77.57	68.5	73.51	71.42	65.36	38.57	86.79	66.9	46.76	93.97	74.34	65.23	56.66	66.26	65.3	69.28	68.06
Approximate GAP + Proto-MAML2023.04		56.65	76.3	67.61	73.29	71.01	63.71	38.3	86.73	66.44	46.54	92.27	75.96	65.94	56.65	65.65	64.66	68.39	67.35
GAP + fo-MAML2023.04		56.33	77.04	68.03	72.01	71.82	64.36	37.46	85.31	67.89	46.87	93.53	74.54	63.23	56.33	65.64	64.71	70.04	67.89
Approximate GAP + fo-MAML2023.04		55.34	75.17	67.4	72.35	71.12	63.49	37.19	84.44	66.82	45.99	93.46	74.05	62.66	55.34	64.89	63.93	69.56	67.25
ALFA + Proto-MAML2023.04		52.8	61.87	63.43	69.75	70.78	59.17	41.49	85.96	60.78	48.11	—	—	—	52.8	62.37	61.41	—	—
ALFA + fo-MAML2023.04		51.09	67.89	66.34	67.67	65.34	60.53	37.41	84.28	60.86	40.05	—	—	—	51.09	61.15	60.15	—	—
Proto-MAML2023.04		49.53	63.37	55.95	68.66	66.49	51.52	39.96	87.15	48.83	43.74	—	—	—	49.53	58.41	57.52	—	—
fo-MAML2023.04		45.51	55.55	56.24	63.61	68.04	43.96	32.1	81.74	50.93	35.3	—	—	—	45.51	54.16	53.3	—	—