Training a neural network for a classification task typically assumes that the data to train are given from the beginning. However, in the real world, additional data accumulate gradually and the model requires additional training without accessing the old training data. This usually leads to the catastrophic forgetting problem which is inevitable for the traditional training methodology of neural networks. In this paper, we propose a memory-based continual learning method that is able to learn additional tasks while retaining the performance of previously learned tasks. Composed of two complementary networks, the Hippocampus-net (H-net) and the Cortex-net (C-net), our model estimates the index of the corresponding task for an input sample and utilizes a particular portion of itself with the estimated index. The C-net guarantees no degradation in the performance of the previously learned tasks and the H-net shows high confidence in finding the origin of the input sample

本文提出了一种连续学习方法，通过堆叠参数实现对额外任务的学习并保留先前任务的性能，其中StackNet保证先前学习任务的性能不降低，而索引模块则表现出在找到输入样本的来源时的高置信度。与PackNet相比，该方法竞争力强且高度直观。

StackNet: 续学习的参数堆叠