Meta-learning aims to solve unseen tasks with few labelled instances. Nevertheless, despite its effectiveness for quick learning in existing optimization-based methods, it has several flaws. Inconsequential connections are frequently seen during meta-training, which results in an over-parameterized neural network. Because of this, meta-testing observes unnecessary computations and extra memory overhead. To overcome such flaws. We propose a novel meta-learning method called Meta-LTH that includes indispensible (necessary) connections. We applied the lottery ticket hypothesis technique known as magnitude pruning to generate these crucial connections that can effectively solve few-shot learning problem. We aim to perform two things: (a) to find a sub-network capable of more adaptive meta-learning and (b) to learn new low-level features of unseen tasks and recombine those features with the already learned features during the meta-test phase. Experimental results show that our proposed Met-LTH method outperformed existing first-order MAML algorithm for three different classification datasets. Our method improves the classification accuracy by approximately 2% (20-way 1-shot task setting) for omniglot dataset.

翻译：元学习旨在通过少量标记样本解决未见过的任务。然而，尽管现有基于优化的方法在快速学习方面效果显著，但仍存在若干缺陷。在元训练过程中频繁出现无关连接，导致神经网络过度参数化。这导致元测试阶段产生不必要的计算和额外内存开销。为克服这些缺陷，我们提出一种名为Meta-LTH的新型元学习方法，该方法包含不可或缺（必要）的连接。我们应用称为幅度剪枝的彩票假说技术来生成这些关键连接，从而有效解决小样本学习问题。我们的目标是实现两件事：(a) 寻找能够实现更具适应性元学习的子网络；(b) 在元测试阶段学习未见任务的新低级特征，并将这些特征与已学特征重新组合。实验结果表明，我们提出的Meta-LTH方法在三个不同分类数据集上均优于现有的一阶MAML算法。在Omniglot数据集的20-way 1-shot任务设置下，我们的方法将分类准确率提升了约2%。