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Few-Shot Image Classification 벤치마크

Few-Shot Image Classification on Mini-Imagenet 10-way (1-shot)

28개 결과 · ⬇ CSV · JSON

Accuracy

31.1 40.45 49.8 59.15 68.5 2017-03 2026-09 MAML + Transduction — 31.8 (2017-03-09) MAML — 31.3 (2017-03-09) MAML + Transduction — 31.8 (2017-03-09) MAML — 31.3 (2017-03-09) Prototypical Networks (Higher Way) — 34.6 (2017-03-15) Prototypical Networks — 32.9 (2017-03-15) Prototypical Networks (Higher Way) — 34.6 (2017-03-15) Prototypical Networks — 32.9 (2017-03-15) Relation Networks — 34.9 (2017-11-16) Relation Networks — 34.9 (2017-11-16) Reptile+BN — 32.0 (2018-03-08) Reptile — 31.1 (2018-03-08) Reptile+BN — 32.0 (2018-03-08) Reptile — 31.1 (2018-03-08) TPN (Higher Shot) — 38.4 (2018-05-25) Label Propagation — 35.2 (2018-05-25) TPN (Higher Shot) — 38.4 (2018-05-25) Label Propagation — 35.2 (2018-05-25) Simple CNAPS + FETI — 63.5 (2019-12-07) Simple CNAPS — 37.1 (2019-12-07) Simple CNAPS + FETI — 63.5 (2019-12-07) Simple CNAPS — 37.1 (2019-12-07) Transductive CNAPS + FETI — 68.5 (2020-06-17) Transductive CNAPS — 42.8 (2020-06-17) Transductive CNAPS + FETI — 68.5 (2020-06-17) Transductive CNAPS — 42.8 (2020-06-17) TIM-GD — 56.1 (2020-08-25) TIM-GD — 56.1 (2020-08-25) MAML + Transduction — 31.8 (2017-03-09) Prototypical Networks (Higher Way) — 34.6 (2017-03-15) Relation Networks — 34.9 (2017-11-16) TPN (Higher Shot) — 38.4 (2018-05-25) Simple CNAPS + FETI — 63.5 (2019-12-07) Transductive CNAPS + FETI — 68.5 (2020-06-17)
RankModel Accuracy PaperCodeYear
21 Label Propagation 35.2 Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning csyanbin/TPN · csyanbin/TPN-pytorch 2018
22 Relation Networks 34.9 Learning to Compare: Relation Network for Few-Shot Learning sicara/easy-few-shot-learning · floodsung/LearningToCompare_FSL · lzrobots/LearningToCompare_ZSL · +10 2017
23 Prototypical Networks (Higher Way) 34.6 Prototypical Networks for Few-shot Learning learnables/learn2learn · oscarknagg/few-shot · sicara/easy-few-shot-learning · +40 2017
24 Prototypical Networks 32.9 Prototypical Networks for Few-shot Learning learnables/learn2learn · oscarknagg/few-shot · sicara/easy-few-shot-learning · +40 2017
25 Reptile+BN 32.0 On First-Order Meta-Learning Algorithms learnables/learn2learn · MaximeVandegar/Papers-in-100-Lines-of-Code · openai/supervised-reptile · +10 2018
26 MAML + Transduction 31.8 Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks ray-project/ray · PaddlePaddle/PaddleRec · learnables/learn2learn · +82 2017
27 MAML 31.3 Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks ray-project/ray · PaddlePaddle/PaddleRec · learnables/learn2learn · +82 2017
28 Reptile 31.1 On First-Order Meta-Learning Algorithms learnables/learn2learn · MaximeVandegar/Papers-in-100-Lines-of-Code · openai/supervised-reptile · +10 2018
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