GEMS: Generating Efficient Meta-Subnets

Gradient-based meta learners (GBML) such as MAML [6] aim to learn a model initialization across similar tasks, such that the model generalizes well on unseen tasks sampled from the same distribution with few gradient updates. A limitation of GBML is its inability to adapt to real-world applications where input tasks are sampled from multiple distributions. An existing effort [23] learns ${\mathcal{N}}$ initializations for tasks sampled from ${\mathcal{N}}$ distributions; roughly increasing training time by a factor of ${\mathcal{N}}$. Instead, we use a single model initialization to learn distribution-specific parameters for every input task. This reduces negative knowledge transfer across distributions and overall computational cost. Specifically, we explore two ways of efficiently learning on multi-distribution tasks: 1) Binary Mask Perceptron (BMP) which learns distribution-specific layers, 2) Multi-modal Supermask (MMSUP) which learns distribution-specific parameters. We evaluate the performance of the proposed framework (GEMS) on few-shot vision classification tasks. The experimental results demonstrate an improvement in accuracy and a speed-up of ~2× to 4× in the training time, over existing state of the art algorithms on quasi-benchmark datasets in the field of meta-learning.