B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-Learning

TL;DR

This paper introduces B-SMALL, a Bayesian neural network extension of MAML that incorporates sparsity to improve generalization and reduce model complexity in meta-learning tasks.

Contribution

It proposes a novel Bayesian MAML framework with a sparsifying regularizer, enhancing model efficiency and performance in few-shot learning scenarios.

Findings

Improved model sparsity without sacrificing accuracy

Enhanced generalization in meta-learning tasks

Effective in distributed sensor network applications

Abstract

There is a growing interest in the learning-to-learn paradigm, also known as meta-learning, where models infer on new tasks using a few training examples. Recently, meta-learning based methods have been widely used in few-shot classification, regression, reinforcement learning, and domain adaptation. The model-agnostic meta-learning (MAML) algorithm is a well-known algorithm that obtains model parameter initialization at meta-training phase. In the meta-test phase, this initialization is rapidly adapted to new tasks by using gradient descent. However, meta-learning models are prone to overfitting since there are insufficient training tasks resulting in over-parameterized models with poor generalization performance for unseen tasks. In this paper, we propose a Bayesian neural network based MAML algorithm, which we refer to as the B-SMALL algorithm. The proposed framework incorporates a sparse variational loss term alongside the loss function of MAML, which uses a sparsifying approximated KL divergence as a regularizer. We demonstrate the performance of B-MAML using classification and regression tasks, and highlight that training a sparsifying BNN using MAML indeed improves the parameter footprint of the model while performing at par or even outperforming the MAML approach. We also illustrate applicability of our approach in distributed sensor networks, where sparsity and meta-learning can be beneficial.