Decoder Choice Network for Meta-Learning

Meta-learning has been widely used for implementing few-shot learning and fast model adaptation. One kind of meta-learning methods attempt to learn how to control the gradient descent process in order to make the gradient-based learning have high speed and generalization... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Few-Shot Image Classification OMNIGLOT - 1-Shot, 20-way DCN6-E Accuracy 99.11 # 2
Few-Shot Image Classification OMNIGLOT - 1-Shot, 20-way DCN4 Accuracy 98.8% # 3
Few-Shot Image Classification OMNIGLOT - 1-Shot, 5-way DCN4 Accuracy 99.8% # 3
Few-Shot Image Classification OMNIGLOT - 1-Shot, 5-way DCN6-E Accuracy 99.92% # 2
Few-Shot Image Classification OMNIGLOT - 5-Shot, 20-way DCN4 Accuracy 99.5% # 3
Few-Shot Image Classification OMNIGLOT - 5-Shot, 20-way DCN6-E Accuracy 99.63 # 2
Few-Shot Image Classification OMNIGLOT - 5-Shot, 5-way DCN4 Accuracy 99.89% # 4
Few-Shot Image Classification OMNIGLOT - 5-Shot, 5-way DCN6-E Accuracy 99.92% # 1

Methods used in the Paper


METHOD TYPE
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