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awesome-deep-learning-survey-papers

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In this page, I will provide a list of survey papers on topics related to deep learning and its applications in various fields.

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# Awesome- Deep Learning Related Survey Papers

In this page, I will provide a curated list of survey papers on topics related to deep learning and its applications in various fields, including computer vision, natural language processing, speech recognition, healthcare and more. 
Due to the large volume of works published in deep learning area, I try to keep track of comprehensive survey papers published (or under review) in top journals. 
If there is a paper missing here, please email me (at shervin.minaee@gmail.com) the paper title and journal, and I would be happy to include it in this list.

# Background

Deep learning models have been applied to various tasks/application from different areas in the past 8-9 years, and there are countless papers written on the applications of deep learning in different fields. 
With the maturity and rising number of deep learning works in different fields, people have started writing survey papers to provide a review of the most promising works in those topics.

# Table of Contents

[1. Computer Vision Related Surveys](#cv) 
[2. NLP Related Surveys](#nlp) 
[3. Healthcare Related Surveys](#hc) 
[4. Other Areas Surveys](#ot)

### 1. Computer Vision Related Surveys
Here we provide a list of surveys for deep learning applications in computer vision.
* Diffusion Models in Vision:
 * [Diffusion models in vision: A survey](https://ieeexplore.ieee.org/abstract/document/10081412) (PAMI, 2023) 
 * [Text-to-image diffusion model in generative ai: A survey](https://arxiv.org/abs/2303.07909) (arXiv, 2023)
 * [Diffusion models: A comprehensive survey of methods and applications](https://dl.acm.org/doi/abs/10.1145/3626235) (ACM CSUR, 2023)
* Object Detection:
 * [Deep Learning for Generic Object Detection: A Survey](https://arxiv.org/pdf/1809.02165.pdf) (IJCV, 2019) 
 * [Object Detection with Deep Learning: A Review](https://arxiv.org/pdf/1807.05511.pdf) (IEEE TNNLS, 2019) 
 * [A Survey of Deep Learning-Based Object Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8825470) (IEEE Access, 2019)
 * [Recent advances in deep learning for object detection](https://www.sciencedirect.com/science/article/abs/pii/S0925231220301430) (Neurocomputing, 2020)
* Image Segmentation: 
 * [Image Segmentation Using Deep Learning: A Survey](https://arxiv.org/pdf/2001.05566.pdf) (IEEE PAMI, 2021)
 * [A survey on deep learning techniques for image and video semantic segmentation](https://e-tarjome.com/storage/panel/fileuploads/2019-06-15/1560581890_E11312-e-tarjome.pdf) (Applied Soft Computing, 2018)
* Medical Image Segmentation:
 * [Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges](https://link.springer.com/article/10.1007/s10278-019-00227-x) (Journal of Digital Imaging, 2018) 
 * [A review: Deep learning for medical image segmentation using multi-modality fusion](https://www.sciencedirect.com/science/article/pii/S2590005619300049) (Elsevier Array, 2019)
 * [Automatic breast ultrasound image segmentation: A survey](https://arxiv.org/ftp/arxiv/papers/1704/1704.01472.pdf) (Pattern Recognition, 2018)
* Face Recognition:
 * [Deep Face Recognition: A Survey](https://arxiv.org/pdf/1804.06655.pdf) (Arxiv, 2019)
 * [Face recognition systems: A survey](https://www.mdpi.com/1424-8220/20/2/342) (Sensors, 2020)
* Biometrics Recognition:
 * [Biometric Recognition Using Deep Learning: A Survey](https://arxiv.org/pdf/1912.00271.pdf) (AIRE, 2022)
 * [Deep learning for biometrics: A survey](https://dl.acm.org/doi/abs/10.1145/3190618) (ACM CSUR, 2018)
* Image Super-Resolution:
 * [Deep learning for image super-resolution: A survey](https://arxiv.org/pdf/1902.06068.pdf) (PAMI, 2020)
 * [A deep journey into super-resolution: A survey](https://dl.acm.org/doi/abs/10.1145/3390462) (ACM CSUR, 2020)
* Facial Expression Recognition:
 * [Deep facial expression recognition: A survey](https://arxiv.org/pdf/1804.08348.pdf) (Arxiv, 2019)
* Action Recognition:
 * [Going deeper into action recognition: A survey](https://arxiv.org/pdf/1605.04988.pdf) (Image and vision computing, 2017)
 * [Human action recognition and prediction: A survey](https://link.springer.com/article/10.1007/s11263-022-01594-9) (IJCV, 2022)
* GANs:
 * [Generative Adversarial Networks: A Survey and Taxonomy](https://arxiv.org/pdf/1906.01529.pdf) (Arxiv, 2019) 
 * [Generative adversarial networks: A survey toward private and secure applications](https://dl.acm.org/doi/abs/10.1145/3459992) (ACM CSUR, 2021) 
* Image Captioning:
 * [A comprehensive survey of deep learning for image captioning](https://arxiv.org/pdf/1810.04020.pdf) (ACM Computing Surveys, 2019) 
* Human Motion Recognition:
 * [RGB-D-based human motion recognition with deep learning: A survey](https://arxiv.org/pdf/1711.08362.pdf) (Computer Vision and Image Understanding, 2018)
* Neural Rendering:
 * [State of the Art on Neural Rendering](https://onlinelibrary.wiley.com/doi/am-pdf/10.1111/cgf.14022) (Computer Graphics Forum, 2020)

### 2. NLP Related Surveys 
NLP tasks have seen a great progress over the past few years with the help of deep neural architectures, such as attentional LSTM, Transformer, BERT, GPT models, and XL-Net. 
* Large Language Models:
 * [Large language models: A survey](https://arxiv.org/abs/2402.06196) (arXiv, 2024)
 * [A survey of large language models](https://arxiv.org/abs/2303.18223) (arXiv, 2023)
* Transformers:
 * [A survey of transformers](https://www.sciencedirect.com/science/article/pii/S2666651022000146) (AI Open, 2022)
 * [Efficient transformers: A survey](https://dl.acm.org/doi/full/10.1145/3530811) (ACM CSUR, 2022) 
* General NLP:
 * [Deep Learning Based Text Classification: A Comprehensive Review](https://arxiv.org/pdf/2004.03705.pdf) (ACM Computing Surveys, 2021)
 * [Recent trends in deep learning based natural language processing](https://arxiv.org/pdf/1708.02709.pdf) (IEEE Computational intelligence magazine, 2018) 
* Dialogue Systems, Conversational AI:
 * [Neural approaches to conversational AI](https://www.nowpublishers.com/article/DownloadSummary/INR-074) (Foundations and Trends® in Information Retrieval, 2019) 
 * [A survey of natural language generation techniques with a focus on dialogue systems-past, present and future directions](https://arxiv.org/pdf/1812.02303.pdf) (Arxiv, 2018) 
 * [A survey on dialogue systems: Recent advances and new frontiers](https://arxiv.org/pdf/1711.01731.pdf) (Acm Sigkdd Explorations Newsletter, 2017)
* Embedding:
 * [Word embeddings: A survey](https://arxiv.org/pdf/1901.09069.pdf) (Arxiv, 2019) 
 * [Neural Information Retrieval: A Literature Review](https://arxiv.org/pdf/1611.06792.pdf) (Arxiv, 2016) 
* Natural Language Generation:
 * [Survey of the state of the art in natural language generation: Core tasks, applications and evaluation](https://www.jair.org/index.php/jair/article/download/11173/26378) (Journal of Artificial Intelligence Research, 2018) 
* Text Summarization:
 * [Neural abstractive text summarization with sequence-to-sequence models](https://arxiv.org/pdf/1906.00500.pdf) (Arxiv, 2019) 
* Sentiment Analysis:
 * [Deep learning for sentiment analysis: A survey](https://arxiv.org/ftp/arxiv/papers/1801/1801.07883.pdf) (Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2018) 
 * [Deep learning for aspect-based sentiment analysis: a comparative review](https://www.sciencedirect.com/science/article/pii/S0957417418306456) (Expert Systems with Applications, 2019)
 * [A survey of sentiment analysis in social media](https://link.springer.com/article/10.1007/s10115-018-1236-4)(Knowledge and Information Systems, 2019)
* Named Entity Recognition:
 * [A survey on recent advances in named entity recognition from deep learning models](https://arxiv.org/pdf/1910.11470) (Arxiv, 2019)
 * [A survey on deep learning for named entity recognition](https://arxiv.org/pdf/1812.09449.pdf) (Arxiv, 2018)
* Answer Selection:
 * [A review on deep learning techniques applied to answer selection](https://www.aclweb.org/anthology/C18-1181.pdf) (Proceedings of the 27th International Conference on Computational Linguistics, 2018) 
* NLP based Financial Forecasting: 
 * [Natural language based financial forecasting: a survey](https://dspace.mit.edu/bitstream/handle/1721.1/116314/10462_2017_9588_ReferencePDF.pdf?sequence=2&isAllowed=y) (Artificial Intelligence Review, 2018)

### 3. Healthcare Related Surveys
Deep learning models 
* Medical Image Analysis:
 * [A survey on deep learning in medical image analysis](https://www.sciencedirect.com/science/article/abs/pii/S1361841517301135) (Medical image analysis, 2017)
* Health-Record Analysis:
 * [Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis](https://europepmc.org/article/PMC/6043423) ( IEEE journal of biomedical and health informatics, published, 2017)
* Bioinformatics:
 * [A survey of data mining and deep learning in bioinformatics](https://link.springer.com/article/10.1007/s10916-018-1003-9) (Journal of medical systems, 2018)
* Medical Imaging (MRI):
 * [An overview of deep learning in medical imaging focusing on MRI](https://www.sciencedirect.com/science/article/pii/S0939388918301181) (Zeitschrift für Medizinische Physik, 2019)
* Radiotherapy:
 * [Survey on deep learning for radiotherapy](https://www.sciencedirect.com/science/article/abs/pii/S0010482518301318) (Computers in biology and medicine, 2018)
* Pharmacogenomics:
 * [Deep learning in pharmacogenomics: from gene regulation to patient stratification](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022084/) (Future Medicine, 2019)
* Cancer Detection and Diagnosis:
 * [Deep learning for image-based cancer detection and diagnosis− a survey](https://www.sciencedirect.com/science/article/abs/pii/S0031320318301845) (Pattern Recognition, 2018)
* Microscopy Image Analysis:
 * [Deep learning in microscopy image analysis: A survey](https://ieeexplore.ieee.org/abstract/document/8118310) (EEE Transactions on Neural Networks and Learning Systems, 2017)
* Radialogy:
 * [Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI](https://arxiv.org/pdf/1802.08717.pdf) (Journal of magnetic resonance imaging, 2019)

### 4. Other Areas Surveys
Deep learning models have been used in various other fields in recent years. Some of the most popular ones includes:
* Reinforcement Learning:
  * [Deep reinforcement learning: An overview](https://arxiv.org/abs/1701.07274) (Arxiv, 2017)
  * [Deep reinforcement learning: A brief survey](https://discovery.ucl.ac.uk/id/eprint/10083557/1/1708.05866v2.pdf) (IEEE Signal Processing Magazine, 2017)
* Recommender Systems:
  * [Deep learning based recommender system: A survey and new perspectives](https://arxiv.org/pdf/1707.07435.pdf) (ACM Computing Surveys, 2019)
* Big Data:
  * [A survey on deep learning for big data](https://fardapaper.ir/mohavaha/uploads/2018/07/Fardapaper-A-survey-on-deep-learning-for-big-data.pdf) (Information Fusion, 2018)
* Networking:
  * [Deep learning in mobile and wireless networking: A surve](https://arxiv.org/pdf/1803.04311.pdf) (IEEE Communications Surveys, 2019)
* IoT Big Data:
  * [Deep learning for IoT big data and streaming analytics: A survey](https://arxiv.org/pdf/1712.04301;Deep) (Deep learning for IoT big data and streaming analytics: A survey., 2019)
* Anomly Detection:
  * [A survey of deep learning-based network anomaly detection](https://link.springer.com/article/10.1007/s10586-017-1117-8) (Cluster Computing, 2017)
* Remote sensing:
  * [Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community](https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-11/issue-04/042609/Comprehensive-survey-of-deep-learning-in-remote-sensing--theories/10.1117/1.JRS.11.042609.full?sessionGUID=eb3317d0-d639-c4d8-a52b-0c0ec58493e0&webSyncID=a982a00b-dac6-2568-3bb6-383dd8bb4517&sessionGUID=e0c58d1c-a696-1478-2588-a8a194890dcc) (Journal of Applied Remote Sensing, 2017)
* Mobile Multimedia:
  * [Deep learning for mobile multimedia: A survey](https://www.researchgate.net/profile/Minh_Dao11/publication/318081382_Deep_Learning_for_Mobile_Multimedia_A_Survey/links/59e3f9bd0f7e9b97fbeb0989/Deep-Learning-for-Mobile-Multimedia-A-Survey.pdf) ( ACM Transactions on Multimedia Computing, Communications, and Applications, 2017)
* Graphs:
  * [Deep learning on graphs: A survey](https://arxiv.org/pdf/1812.04202.pdf%E3%80%82) (Arxiv, 2018)
  * [Graph embedding techniques, applications, and performance: A survey](https://www.sciencedirect.com/science/article/abs/pii/S0950705118301540) (Knowledge-Based Systems, 2018)
* Multimodal Learning:
  * [Deep multimodal learning: A survey on recent advances and trends](https://ieeexplore.ieee.org/abstract/document/8103116/) (IEEE Signal Processing Magazine, 2017)
  * [Deep multimodal representation learning: A survey](https://ieeexplore.ieee.org/abstract/document/8715409/) (IEEE Access, 2019)
* Agriculture:
  * [Deep learning in agriculture: A survey](https://arxiv.org/ftp/arxiv/papers/1807/1807.11809.pdf) (Computers and electronics in agriculture, 2018)

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