Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels

Recently, different machine learning methods have been introduced to tackle the challenging few-shot learning scenario that is, learning from a small labeled dataset related to a specific task. Common approaches have taken the form of meta-learning: learning to learn on the new problem given the old... (read more)

PDF Abstract NeurIPS 2020 PDF NeurIPS 2020 Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK BENCHMARK
Few-Shot Image Classification CUB 200 5-way 1-shot DKT + BNCosSim Accuracy 72.27 # 10
Few-Shot Image Classification CUB 200 5-way 5-shot DKT + BNCosSim Accuracy 85.64 # 10
Few-Shot Image Classification Mini-Imagenet 5-way (1-shot) DKT + BNCosSim Accuracy 62.96 # 26
Few-Shot Image Classification Mini-Imagenet 5-way (5-shot) DKT + BNCosSim Accuracy 64.0 # 55
Few-Shot Image Classification Mini-ImageNet-CUB 5-way (1-shot) DKT + CosSim Accuracy 40.22 # 6
Few-Shot Image Classification Mini-ImageNet-CUB 5-way (5-shot) DKT + BNCosSim Accuracy 56.40 # 5
Few-Shot Image Classification OMNIGLOT-EMNIST 5-way (1-shot) DKT + BNCosSim Accuracy 75.40 # 1
Few-Shot Image Classification OMNIGLOT-EMNIST 5-way (5-shot) DKT + BNCosSim Accuracy 90.3 # 1

Methods used in the Paper


METHOD TYPE
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