Learning to Compare: Relation Network for Few-Shot Learning

CVPR 2018 Flood SungYongxin YangLi ZhangTao XiangPhilip H. S. TorrTimothy M. Hospedales

We present a conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each. Our method, called the Relation Network (RN), is trained end-to-end from scratch... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Few-Shot Image Classification CIFAR-FS 5-way (5-shot) Relation Networks* Accuracy 69.3 # 10
Few-Shot Image Classification CUB 200 5-way 1-shot Relation Net Accuracy 50.44 # 14
Few-Shot Image Classification CUB 200 5-way 5-shot Relation Net Accuracy 65.32 # 13
Few-Shot Image Classification Mini-Imagenet 5-way (1-shot) Relation Net (Sung et al., 2018) Accuracy 50.4 # 37
Few-Shot Image Classification OMNIGLOT - 1-Shot, 20-way Relation Net Accuracy 97.6% # 6
Few-Shot Image Classification OMNIGLOT - 1-Shot, 5-way Relation Net Accuracy 99.6 # 4
Few-Shot Image Classification OMNIGLOT - 5-Shot, 20-way Relation Net Accuracy 99.1% # 8
Few-Shot Image Classification OMNIGLOT - 5-Shot, 5-way Relation Net Accuracy 99.8 # 5

Results from Other Papers


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK SOURCE PAPER COMPARE
Few-Shot Image Classification Mini-ImageNet-CUB 5-way (1-shot) RelationNet (Sung et al., 2018) Accuracy 42.91 # 5
Image Classification Tiered ImageNet 5-way (5-shot) Relation Net Accuracy 71.31 # 2

Methods used in the Paper


METHOD TYPE
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