Improving unbalanced image classification through fine-tuning method of reinforcement learning

Image classification, especially unbalanced image classification, holds considerable promise for practical applications. Existing research focuses mainly on enhancing the effectiveness of classifiers through approaches such as data resampling and loss function adjustments. To date, most of the approaches address the class unbalanced issue by transforming unbalanced data distributions into balanced [id=SecondEdit]ones. Consequently, a critical challenge is to directly develop high-performance classifiers that are adaptive to diverse unbalanced data distributions. In this paper, for unbalanced image classification tasks, we propose a Reinforcement Learning Fine-tuning approach to Unbalanced image Classification (RLF-UC). Specifically, we train classification pretraining models on [d=FirstEdit]fivethree unbalanced datasetsid=SecondEdit], and train [d=FirstEdit]the correspondinga reward function model designed to optimize fine-tuning policy. Then, we train a reinforcement learning fine-tuning classification model and optimize its policy to maximize the cumulative expected reward. Finally, we guide the model to prioritize minority category knowledge and incorporate distribution distance constraints, which are derived from the disparities between the [d=SecondEdit]pre-trainedpretrained model and the fine-tuning model to dynamically adjust the fine-tuning classifier model. Experimental results on [d=FirstEdit]fivethree reprocessed unbalanced image datasets demonstrate that our RLF-UC method provides comparable or better classification and generalization capabilities than other baselines.