Gaussian Process Meta Few-shot Classifier Learning via Linear Discriminant Laplace Approximation

Understand

The meta learning few-shot classification is an emerging problem in machine learning that received enormous attention recently, where the goal is to learn a model that can quickly adapt to a new task with only a few labeled data.

• We consider the Bayesian Gaussian process (GP) approach, in which we meta-learn the GP prior, and the adaptation to a new task is carried out by the GP predictive model from the posterior inference.
• We adopt the Laplace posterior approximation, but to circumvent the iterative gradient steps for finding the MAP solution, we introduce a novel linear discriminant analysis (LDA) plugin as a surrogate for the MAP solution.
• In essence, the MAP solution is approximated by the LDA estimate, but to take the GP prior into account, we adopt the prior-norm adjustment to estimate LDA's shared variance parameters, which ensures that the adjusted estimate is consistent with the GP prior.