论文标题
Pac-Bayes元学习，具有隐性特定任务的后代
PAC-Bayes meta-learning with implicit task-specific posteriors
论文作者
论文摘要
我们介绍了一种新的且严格的Pac-Bayes元学习算法，该算法解决了几乎没有学习的学习。我们提出的方法将Pac-Bayes框架从单个任务设置扩展到元学习多个任务设置，再到在任何（甚至看不见）的任务和样本上评估的错误。我们还提出了一种基于生成的方法，以与基于对角线协方差矩阵的多变量正态分布相比，与通常的假设相比，更有表现地估算特定于任务模型参数的后验。我们表明，经过我们提出的元学习算法训练的模型经过良好校准和准确，并在很少的射击分类（Mini-ImageNet和Tiered-ImageNet）和回归（多模式任务 - 分布回归）上进行了最新的校准和分类结果。
We introduce a new and rigorously-formulated PAC-Bayes meta-learning algorithm that solves few-shot learning. Our proposed method extends the PAC-Bayes framework from a single task setting to the meta-learning multiple task setting to upper-bound the error evaluated on any, even unseen, tasks and samples. We also propose a generative-based approach to estimate the posterior of task-specific model parameters more expressively compared to the usual assumption based on a multivariate normal distribution with a diagonal covariance matrix. 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.
