A Survey of Deep Active Learning Deep active learning (DeepAL) combines deep learning (DL) and active learning (AL) to reduce the cost of sample annotation while maintaining the powerful learning capabilities of DL. DL requires large amounts of labeled data for training, but AL selects the most informative samples for labeling, reducing the need for extensive annotations. However, traditional AL has been less popular due to its limited effectiveness with high-dimensional data. DeepAL addresses this by integrating DL's feature extraction capabilities with AL's query strategies, enabling efficient learning in data-rich environments. DL has made significant progress in various fields due to the availability of large annotated datasets. However, obtaining high-quality labeled data is costly and challenging in specialized domains. DeepAL aims to reduce this cost by using AL to select the most informative samples for labeling. This approach has been widely applied in image recognition, text classification, visual question answering, and object detection. The paper provides a comprehensive survey of DeepAL, discussing its challenges, strategies, and applications. It highlights the importance of query strategies in AL, such as uncertainty-based, diversity-based, and hybrid methods, which aim to balance information and diversity in sample selection. DeepAL also addresses the issue of model uncertainty, using Bayesian methods to improve confidence in predictions. The paper also discusses the challenges of combining DL and AL, including model uncertainty, insufficient labeled data, and inconsistent processing pipelines. It proposes solutions such as Bayesian deep learning, data augmentation, and hybrid query strategies to overcome these challenges. The survey categorizes DeepAL methods into different types, including batch-based, uncertainty-based, and density-based approaches. It also discusses the importance of automated design in DeepAL, where machine learning techniques are used to optimize both DL models and AL query strategies. Overall, DeepAL offers a promising approach to reduce the cost of sample annotation while maintaining the performance of DL. The survey highlights the potential of DeepAL in various applications and suggests future research directions, including the development of more efficient query strategies and the integration of deep learning with active learning in new contexts.A Survey of Deep Active Learning Deep active learning (DeepAL) combines deep learning (DL) and active learning (AL) to reduce the cost of sample annotation while maintaining the powerful learning capabilities of DL. DL requires large amounts of labeled data for training, but AL selects the most informative samples for labeling, reducing the need for extensive annotations. However, traditional AL has been less popular due to its limited effectiveness with high-dimensional data. DeepAL addresses this by integrating DL's feature extraction capabilities with AL's query strategies, enabling efficient learning in data-rich environments. DL has made significant progress in various fields due to the availability of large annotated datasets. However, obtaining high-quality labeled data is costly and challenging in specialized domains. DeepAL aims to reduce this cost by using AL to select the most informative samples for labeling. This approach has been widely applied in image recognition, text classification, visual question answering, and object detection. The paper provides a comprehensive survey of DeepAL, discussing its challenges, strategies, and applications. It highlights the importance of query strategies in AL, such as uncertainty-based, diversity-based, and hybrid methods, which aim to balance information and diversity in sample selection. DeepAL also addresses the issue of model uncertainty, using Bayesian methods to improve confidence in predictions. The paper also discusses the challenges of combining DL and AL, including model uncertainty, insufficient labeled data, and inconsistent processing pipelines. It proposes solutions such as Bayesian deep learning, data augmentation, and hybrid query strategies to overcome these challenges. The survey categorizes DeepAL methods into different types, including batch-based, uncertainty-based, and density-based approaches. It also discusses the importance of automated design in DeepAL, where machine learning techniques are used to optimize both DL models and AL query strategies. Overall, DeepAL offers a promising approach to reduce the cost of sample annotation while maintaining the performance of DL. The survey highlights the potential of DeepAL in various applications and suggests future research directions, including the development of more efficient query strategies and the integration of deep learning with active learning in new contexts.