Incremental Learning Model Based on Ensemble Learning

Currently, deep learning neural networks have shown remarkable performance in the field of image classification. However, existing classification networks are typically applied within closed environments, where all collected samples are batch-trained to produce classification results. In practical applications, the categories of samples are not static but dynamically increase over time. With the continual addition of new classes, the current classification methods exhibit limited continual learning capabilities, making them unsuitable for applications with stringent real-time requirements. To address this issue, this paper investigates an incremental learning algorithm based on ensemble transfer learning to handle classification training for newly added categories. This algorithm leverages the strengths of ensemble transfer learning to enhance the network's ability to continually learn new categories, enabling fast and accurate classification of new samples. In this paper, the proposed algorithm is applied to the CUB-200-2011 dataset for class-incremental experiments and compared with other deep learning models. Experimental results demonstrate that the incremental learning classification algorithm based on integrated transfer outperforms other models.