Meta-Tasks: An alternative view on Meta-Learning Regularization
Mohammad Rostami Atik Faysal Huaxia Wang Avimanyu Sahoo Ryan Antle
Few-shot learning (FSL) is a challenging machine learning problem due to a
scarcity of labeled data. The ability to generalize effectively on both novel
and training tasks is a significant barrier to FSL. This paper proposes a novel
solution that can generalize to both training and novel tasks while also
utilizing unlabeled samples. The method refines the embedding model before
updating the outer loop using unsupervised techniques as ``meta-tasks''. The
experimental results show that our proposed method performs well on novel and
training tasks, with faster and better convergence, lower generalization, and
standard deviation error, indicating its potential for practical applications
in FSL. The experimental results show that the proposed method outperforms
prototypical networks by 3.9%.