Researches Advanced in Deep Learning based Image Classification

Abstract

Image recognition has always been a popular research topic in computer vision, whose basic task is to learn a model to predict the category of a given image. Early image classification algorithms mainly relied on handcrafted features, while their classification results often failed to meet practical application requirements due to the limitation of handcrafted features expressiveness ability. Thanks to the rapid development of deep learning, image recognition algorithms based on convolutional neural networks have achieved great success. Generally, stacking network layers can improve the prediction accuracy, while increasing the network depth can also lead to problems such as gradient disappearance, gradient explosion, and degradation. In recent years, due to its powerful representation ability, Transformer-based image classification algorithms have achieved new breakthroughs in recognition accuracy. This paper first introduces the classic deep learning algorithms in the field of image classification, including networks such as AlexNet, GoogLeNet, VGG, and ResNet. Meanwhile, the visual transformer (ViT) and the data-efficient image transformer are further introduced to handle image classification tasks. In addition, this paper analyzes the application and development of these two models in image classification, classifies the different models, and analyzes their advantages and disadvantages.