Deep models need a large number of labeled samples to be trained. Furthermore, in practical application settings where objects’ features are added or changed over time, it is difficult and expensive to get enough labeled samples in the beginning. Cognitive learning mechanism can actively raise the deep models’ proficiency online with a few training labels gradually. In this paper, inspired by human being’s cognition procedure to acquire new knowledge stage by stage, we develop a novel deep active recognition framework based on the analysis of models’ cognitive error knowledge to fine-tune the deep models online. The transformation of the cognitive errors is defined, and the corresponding knowledge is obtained to identify the models’ cognitive information. Based on the cognitive knowledge, the sensitive samples are selected to finely tune the models online. To avoid forgetting the previous learned knowledge, the selected prior training samples are used as the refreshening samples at the same time. The experiments demonstrate that the sensitive samples can benefit the target recognition and the cognitive learning mechanism can boost the deep models’ performance efficiently. The characterization of cognitive information can restrain the other samples’ disturbance to the models’ cognition effectively and the online training method can save mass of the time evidently. In conclusion, we introduce this work to provide a trial of thought about the cognitive lifelong learning used in deep learning scenarios.

In deep learning scenarios, a lot of labeled samples are needed to train the models. However, in practical application fields, since the objects to be recognized are complex and non-uniformly distributed, it is difficult to get enough labeled samples at one time. Active learning can actively improve the accuracy with fewer training labels, which is one of the promising solutions to tackle this problem. Inspired by human being's cognition process to acquire additional knowledge gradually, we propose a novel deep active learning method through Cognitive Information Parcels (CIPs) based on the analysis of model's cognitive errors and expert's instruction. The transformation of the cognitive parcels is defined, and the corresponding representation feature of the objects is obtained to identify the model's cognitive error information. Experiments prove that the samples, selected based on the CIPs, can benefit the target recognition and boost the deep model's performance efficiently. The characterization of cognitive knowledge can avoid the other samples' disturbance to the cognitive property of the model effectively. We believe that our work could provide a trial of thought about the cognitive knowledge used in deep learning field.