Paper Infomation
Research on Image Recognition Using Deep Learning Techniques
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Author: Shuntao Tang, Wei Chen
Abstract: This study delves into the applications, challenges, and future directions of deep learning techniques in the field of image recognition. Deep learning, particularly Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), has become key to enhancing the precision and efficiency of image recognition. These models are capable of processing complex visual data, facilitating efficient feature extraction and image classification. However, acquiring and annotating high-quality, diverse datasets, addressing imbalances in datasets, and model training and optimization remain significant challenges in this domain. The paper proposes strategies for improving data augmentation, optimizing model architectures, and employing automated model optimization tools to address these challenges, while also emphasizing the importance of considering ethical issues in technological advancements. As technology continues to evolve, the application of deep learning in image recognition will further demonstrate its potent capability to solve complex problems, driving society towards more inclusive and diverse development.
Keywords: Deep Learning Techniques, Image Recognition, Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks
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