论文标题
用于分类和生成的增量学习的协作方法
Collaborative Method for Incremental Learning on Classification and Generation
论文作者
论文摘要
尽管训练有素的深度神经网络在许多任务上表现出了出色的表现，但他们迅速忘记了一旦开始学习其他数据，并提供了先前的数据停止，他们就会忘记了他们学到的知识。在本文中，我们介绍了一种具有属性共享（ICLAS）的新型算法，增量类学习（ICLAS），以使用深层神经网络进行增量类学习。作为其组成部分之一，我们还引入了一种生成模型Incgan，该模型可以生成与训练数据相比的图像增加的图像。在挑战性的数据缺陷环境下，iCLA会逐步训练分类和发电网络。由于ICLAS训练两个网络，因此我们的算法可以多次执行增量类学习。 MNIST数据集上的实验证明了我们算法的优势。
Although well-trained deep neural networks have shown remarkable performance on numerous tasks, they rapidly forget what they have learned as soon as they begin to learn with additional data with the previous data stop being provided. In this paper, we introduce a novel algorithm, Incremental Class Learning with Attribute Sharing (ICLAS), for incremental class learning with deep neural networks. As one of its component, we also introduce a generative model, incGAN, which can generate images with increased variety compared with the training data. Under challenging environment of data deficiency, ICLAS incrementally trains classification and the generation networks. Since ICLAS trains both networks, our algorithm can perform multiple times of incremental class learning. The experiments on MNIST dataset demonstrate the advantages of our algorithm.
