PAC-Bayesian Meta-learning with Implicit Prior and Posterior

5 Mar 2020  ·  Cuong Nguyen, Thanh-Toan Do, Gustavo Carneiro ·

We introduce a new and rigorously-formulated PAC-Bayes few-shot meta-learning algorithm that implicitly learns a prior distribution of the model of interest. Our proposed method extends the PAC-Bayes framework from a single task setting to the few-shot learning setting to upper-bound generalisation errors on unseen tasks and samples... We also propose a generative-based approach to model the shared prior and the posterior of task-specific model parameters more expressively compared to the usual diagonal Gaussian assumption. We show that the models trained with our proposed meta-learning algorithm are well calibrated and accurate, with state-of-the-art calibration and classification results on few-shot classification (mini-ImageNet and tiered-ImageNet) and regression (multi-modal task-distribution regression) benchmarks. read more

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Few-Shot Image Classification Mini-Imagenet 5-way (1-shot) SImPa Accuracy 52.11 # 55
Few-Shot Image Classification Mini-Imagenet 5-way (5-shot) SImPa Accuracy 63.87 # 58
Few-Shot Image Classification Tiered ImageNet 5-way (1-shot) SImPa Accuracy 70.82 # 15
Few-Shot Image Classification Tiered ImageNet 5-way (5-shot) SImPa Accuracy 81.84 # 22

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