Brain inspired lifelong learning model based on neural based learning classifier for underwater data classification

The general benchmark for success of an artificial intelligence system is its ability to imitate learning of the human brain. The human brain is capable of continuous learning over a lifespan. The learned knowledge is retained, augmented, fine-tuned and reused to perform new future tasks. At present, machine learning models perform well when carefully arranged, balanced and homogenized data is presented. However, most of these models undergo performance degradation when multiple tasks with incremental data are presented. Inspired by learning of the brain, in this study, we propose a lifelong learning model which extracts knowledge and utilizes the previously learned knowledge to solve the current problem. In the proposed model, firstly, we exploit various deep convolution blocks to extract non-trivial features from images, then a code fragment based learning classifier system with a rich knowledge encoding scheme is devised for knowledge extraction, transfer and reuse. We validate the proposed model with 2 incremental learning scenarios: (i) new instances (ii) new classes, on underwater synsets of the benchmark ImageNet dataset. Experiments results which are analyzed by using paired sampled statistical t-test, show that the proposed model outperforms baseline methods as well as deep convolution neural network based methods, with respect to classification accuracy.

Automated summary

The study proposed a lifelong learning model that extracts and reuses knowledge to solve current problems, and experimental results show its superior performance in terms of classification accuracy compared to other methods.