论文标题
记忆互补网络，用于几次班级学习
Memorizing Complementation Network for Few-Shot Class-Incremental Learning
论文作者
论文摘要
很少有课堂学习（FSCIL）旨在仅用几个样本不断学习新概念，这很容易遭受灾难性的遗忘和过度拟合的问题。旧阶级的无法获得和新颖样本的稀缺性使实现保留旧知识和学习新颖概念之间的权衡很大。受到不同模型的启发，我们在学习新颖概念时记住了不同的知识，我们提出了一个记忆的补充网络（MCNET），以整合多个模型，以在新任务中相互补充不同的记忆知识。此外，为了用很少的新样本更新模型，我们开发了一个原型平滑的硬挖三元组（PSHT）损失，以使新样本不仅在当前的任务中彼此远离，而且还将旧分布远离。在三个基准数据集（例如CIFAR100，Miniimagenet和Cub200）上进行了广泛的实验，证明了我们提出的方法的优越性。
Few-shot Class-Incremental Learning (FSCIL) aims at learning new concepts continually with only a few samples, which is prone to suffer the catastrophic forgetting and overfitting problems. The inaccessibility of old classes and the scarcity of the novel samples make it formidable to realize the trade-off between retaining old knowledge and learning novel concepts. Inspired by that different models memorize different knowledge when learning novel concepts, we propose a Memorizing Complementation Network (MCNet) to ensemble multiple models that complements the different memorized knowledge with each other in novel tasks. Additionally, to update the model with few novel samples, we develop a Prototype Smoothing Hard-mining Triplet (PSHT) loss to push the novel samples away from not only each other in current task but also the old distribution. Extensive experiments on three benchmark datasets, e.g., CIFAR100, miniImageNet and CUB200, have demonstrated the superiority of our proposed method.
