基于主动学习的图像分类技术：现状与未来

As one of the important research directions in the field of computer vision, image classification has a wide range of applications. The success of deep learning-based image classification techniques depends on a large amount of annotated data. However, the cost of data annotation is often expensive. Active learning is a machine learning method that aims to achieve the expected model performance with as few high-quality annotated data as possible, and it can alleviate the problem of high annotation costs and difficulty in obtaining a large amount of annotation information in supervised learning tasks. Based on a sample selection strategy, active learning for image classification selects samples from the unlabeled dataset which are informative and thus contribute more to the training of the classification model, in order to update the annotated training data pool. This process is repeated until a given stopping condition is met or the model annotation budget is exhausted. This paper provides a comprehensive survey of the active learning image classification algorithms published in recent years. According the strategies applied in sample data processing and model structure optimization, existing algorithms are classified into three categories: algorithms based on data augmentation, including those using image augmentation to expand the scale of training data or using the differences in image feature interpolation to select high-quality training data; algorithms based on data distribution information, which optimize sample selection strategies based on the characteristics of data distribution; algorithms for optimizing model predictions, including methods for optimizing the acquisition and utilization of deep model prediction information, improving the predictive model structure through the use of generative adversarial networks and reinforcement learning, as well as enhancing model prediction performance based on the Transformer architecture to ensure the reliability of model predictions. In addition, this paper also conducts experimental comparisons on important academic work under various types of active learning image classification algorithms, and analyzes the performance and adaptability of each algorithm on datasets of different scales. Furthermore, this paper discusses the challenges faced by active learning image classification technology and points out future research directions.