Few-Shot Learning with Global Class Representations

In this paper, we propose to tackle the challenging few-shot learning (FSL) problem by learning global class representations using both base and novel class training samples. In each training episode, an episodic class mean computed from a support set is registered with the global representation via a registration module... (read more)

PDF Abstract ICCV 2019 PDF ICCV 2019 Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Few-Shot Image Classification mini-ImageNet - 100-Way GCR Accuracy 39.14 # 1
Few-Shot Image Classification Mini-ImageNet - 1-Shot Learning GCR Accuracy 53.21 # 13
Few-Shot Image Classification OMNIGLOT - 1-Shot, 20-way GCR Accuracy 99.63 # 1
Few-Shot Image Classification OMNIGLOT - 5-Shot, 20-way GCR Accuracy 99.32 # 6

Methods used in the Paper


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