Human being and different species of animals having the skills to gather,
transferring knowledge, processing, fine-tune and generating information
throughout their lifetime. The ability of learning throughout their lifespan is
referred as continuous learning which is using neurocognition mechanism.
Consequently, in real world computational system of incremental learning
autonomous agents also needs such continuous learning mechanism which provide
retrieval of information and long-term memory consolidation. However, the main
challenge in artificial intelligence is that the incremental learning of the
autonomous agent when new data confronted. In such scenarios, the main concern
is catastrophic forgetting(CF), i.e., while learning the sequentially, neural
network underfits the old data when it confronted with new data. To tackle this
CF problem many numerous studied have been proposed, however it is very
difficult to compare their performance due to dissimilarity in their evaluation
mechanism. Here we focus on the comparison of all algorithms which are having
similar type of evaluation mechanism. Here we are comparing three types of
incremental learning methods: (1) Exemplar based methods, (2) Memory based
methods, and (3) Network based method. In this survey paper, methodology
oriented study for catastrophic forgetting in incremental deep neural network
is addressed. Furthermore, it contains the mathematical overview of impact-full
methods which can be help researchers to deal with CF.

通过模拟生物学习机制，文章研究了连续学习以及其在增量深度神经网络中造成的灾难性遗忘问题，并比较了三种增量学习方法的性能。