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Human capacity for "lifelong learning" encompasses a continuous process of acquiring knowledge, adapting to new environments, and developing new skills throughout one's life. In order to bridge the gap between human intelligence and artificial intelligence, an increasing number of researchers have begun to explore the concept of lifelong learning within the field of machine learning, also referred as continuous learning or incremental learning. Incremental learning enables machine to learn from a continuous stream of data, thereby achieving lifelong learning capability. Incremental learning can be categorized into three types based on the different data batch and task settings: Task Incremental Learning (TIL), Domain Incremental Learning (DIL), and Class Incremental Learning (CIL). These scenarios describe the incremental learning challenges within supervised learning and encompass the majority of incremental learning settings. However, there is currently no unified and specific definition for incremental learning situations with the same task and data distribution while in a continuous information flow. Therefore, in this paper, we concentrated on the situation defined as data incremental learning, where all training samples belong to the same task and share the same data distribution. Currently, there is limited in-depth research on data incremental learning. Hence, this study conducts a preliminary experimental exploration of data incremental learning characteristics within the context of image classification task. Moreover, it reviews existing strategies utilizing deep neural networks for incremental learning and compares these methods in the context of data increment learning, offering insights for future research and exploration in the field.
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