Relevant bibliographies by topics / Deep Convolutional Generative Adversarial Networks (DCGAN)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Deep Convolutional Generative Adversarial Networks (DCGAN)'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Deep Convolutional Generative Adversarial Networks (DCGAN)"

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Manimegala M, Gokulraj V, Karisni K, and Manisha S. "Generating Human Face with Dcgan and Gan." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 05 (2024): 1348–54. http://dx.doi.org/10.47392/irjaeh.2024.0186.

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Generative Adversarial Networks (GANs) are prominent in unsupervised learning for their exceptional data-generation capabilities. GANs utilize backpropagation and a competitive process between a Generative Network (G) and a Discriminative Network (D). In this setup, G generates artificial images while D distinguishes real from artificial ones, enhancing G's ability to create realistic images. Deep Convolutional Generative Adversarial Networks (DCGAN) are particularly notable, using a convolutional architecture to produce high-quality human face images. This study trains DCGAN on the CelebFaces
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Tang, Kejia. "Emojis Generation Based on Deep Convolution Generative Adversarial Network." Applied and Computational Engineering 8, no. 1 (2023): 203–9. http://dx.doi.org/10.54254/2755-2721/8/20230126.

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With the development of information technology and mobile communication, people's usage of emoji is increasing. However, designing emojis by artists can be time-consuming and costly. Therefore, this study attempts to use the Deep Convolution Generative Adversarial Network (DCGAN) method in deep learning to automatically generate emojis. DCGAN is a combination of Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN). It introduces convolutional networks into the generative model for unsupervised training, which can improve the learning effect of the generator network. DCGA
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Kumar, Dheeraj, Mayuri A. Mehta, and Indranath Chatterjee. "Empirical Analysis of Deep Convolutional Generative Adversarial Network for Ultrasound Image Synthesis." Open Biomedical Engineering Journal 15, no. 1 (2021): 71–77. http://dx.doi.org/10.2174/1874120702115010071.

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Introduction: Recent research on Generative Adversarial Networks (GANs) in the biomedical field has proven the effectiveness in generating synthetic images of different modalities. Ultrasound imaging is one of the primary imaging modalities for diagnosis in the medical domain. In this paper, we present an empirical analysis of the state-of-the-art Deep Convolutional Generative Adversarial Network (DCGAN) for generating synthetic ultrasound images. Aims: This work aims to explore the utilization of deep convolutional generative adversarial networks for the synthesis of ultrasound images and to
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Xing, Jin. "Exploiting Deep Convolutional Generative Adversarial Network Generated Images for Enhanced Image Classification." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 476–81. http://dx.doi.org/10.62051/vq4pyb84.

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The power of deep neural networks relies heavily on the quantity and quality of training data. However, it is expensive and time consuming for people to collect and annotate data on a large scale. Traditional methods, including modifying the copies of existing data, do not always have the effect, especially in some biomedical fields where some large-size anonymous datasets are generally not publicly available. So, this paper tried to tackle this problem by generating specific data using Deep Convolutional Generative Adversarial Network (DCGAN). DCGAN structure combines convolution and traditio
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Fujioka, Tomoyuki, Mio Mori, Kazunori Kubota, et al. "Breast Ultrasound Image Synthesis using Deep Convolutional Generative Adversarial Networks." Diagnostics 9, no. 4 (2019): 176. http://dx.doi.org/10.3390/diagnostics9040176.

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Deep convolutional generative adversarial networks (DCGANs) are newly developed tools for generating synthesized images. To determine the clinical utility of synthesized images, we generated breast ultrasound images and assessed their quality and clinical value. After retrospectively collecting 528 images of 144 benign masses and 529 images of 216 malignant masses in the breasts, synthesized images were generated using a DCGAN with 50, 100, 200, 500, and 1000 epochs. The synthesized (n = 20) and original (n = 40) images were evaluated by two radiologists, who scored them for overall quality, d
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Thakur, Amey. "Generative Adversarial Networks." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 2307–25. http://dx.doi.org/10.22214/ijraset.2021.37723.

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Abstract: Deep learning's breakthrough in the field of artificial intelligence has resulted in the creation of a slew of deep learning models. One of these is the Generative Adversarial Network, which has only recently emerged. The goal of GAN is to use unsupervised learning to analyse the distribution of data and create more accurate results. The GAN allows the learning of deep representations in the absence of substantial labelled training information. Computer vision, language and video processing, and image synthesis are just a few of the applications that might benefit from these represen
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Xiang, Wenjie, Zhongchang Song, Xuming Peng, et al. "The development of deep convolutional generative adversarial network to synthesize odontocetes' clicks." Journal of the Acoustical Society of America 157, no. 1 (2025): 328–39. https://doi.org/10.1121/10.0034865.

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Odontocetes are capable of dynamically changing their echolocation clicks to efficiently detect targets, and learning their clicking strategy can facilitate the design of man-made detecting signals. In this study, we developed deep convolutional generative adversarial networks guided by an acoustic feature vector (AF-DCGANs) to synthesize narrowband clicks of the finless porpoise (Neophocaena phocaenoides sunameri) and broadband clicks of the bottlenose dolphins (Tursiops truncatus). The average short-time objective intelligibility (STOI), spectral correlation coefficient (Spe-CORR), waveform
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Huo, Yu. "Generate handwritten images based on DCGAN." Applied and Computational Engineering 4, no. 1 (2023): 165–70. http://dx.doi.org/10.54254/2755-2721/4/20230438.

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Nowadays, more and more machines are applied to artificial intelligence, and generate something itself has become more and more available. To augment databases to get a more accurate result from training a model, generating data is necessary. This paper introduces how to generate new handwritten images by training the existing database. The Generative Adversarial Networks model is used as the basic model. Deep Convolutional Generative Adversarial Networks and the MINIST database are applied as the method and training dataset respectively in this research. After 50 epochs by training 256 images
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Liu, Yukai. "Data augmentation-based enhanced fingerprint recognition using deep convolutional generative adversarial network and diffusion models." Applied and Computational Engineering 52, no. 1 (2024): 8–13. http://dx.doi.org/10.54254/2755-2721/52/20241115.

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The progress of fingerprint recognition applications encounters substantial hurdles due to privacy and security concerns, leading to limited fingerprint data availability and stringent data quality requirements. This article endeavors to tackle the challenges of data scarcity and data quality in fingerprint recognition by implementing data augmentation techniques. Specifically, this research employed two state-of-the-art generative models in the domain of deep learning, namely Deep Convolutional Generative Adversarial Network (DCGAN) and the Diffusion model, for fingerprint data augmentation.
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Sharma, Moolchand, Prerna Sharma, Manish Kumar Jha, and Rohan Singh. "MOTION TRANSFER IN VIDEOS USING DCGAN." Innovative Computing and Communication: An International Journal 2, no. 1 (2020): 17–24. https://doi.org/10.5281/zenodo.4743820.

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Motion Transfer has a wide variety of applications, such as creating motion synchronized videos in film industries and video making apps. The research paper presents a novel approach for motion transfer from a source video to the target person. This approach focuses on the video to video translation using various poses generated in the frames of video for translation. The approach makes use of Pose Generation Convolutional Neural Network to synthesize arbitrary poses from source videos and train the pix2pix – DCGAN(Deep Convolutional Generative Adversarial Networks), which is a condition
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Dissertations / Theses on the topic "Deep Convolutional Generative Adversarial Networks (DCGAN)"

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Adhikari, Aakriti. "Skin Cancer Detection using Generative Adversarial Networkand an Ensemble of deep Convolutional Neural Networks." University of Toledo / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1574383625473665.

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Johansson, Philip. "Incremental Learning of Deep Convolutional Neural Networks for Tumour Classification in Pathology Images." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158225.

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Medical doctors understaffing is becoming a compelling problem in many healthcare systems. This problem can be alleviated by utilising Computer-Aided Diagnosis (CAD) systems to substitute doctors in different tasks, for instance, histopa-thological image classification. The recent surge of deep learning has allowed CAD systems to perform this task at a very competitive performance. However, a major challenge with this task is the need to periodically update the models with new data and/or new classes or diseases. These periodical updates will result in catastrophic forgetting, as Convolutional
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Albertazzi, Riccardo. "A study on the application of generative adversarial networks to industrial OCR." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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High performance and nearly perfect accuracy are the standards required by OCR algorithms for industrial applications. In the last years research on Deep Learning has proven that Convolutional Neural Networks (CNNs) are a very powerful and robust tool for image analysis and classification; when applied to OCR tasks, CNNs are able to perform much better than previously adopted techniques and reach easily 99% accuracy. However, Deep Learning models' effectiveness relies on the quality of the data used to train them; this can become a problem since OCR tools can run for months without interrupti
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Lidberg, Love. "Object Detection using deep learning and synthetic data." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-150555.

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This thesis investigates how synthetic data can be utilized when training convolutional neural networks to detect flags with threatening symbols. The synthetic data used in this thesis consisted of rendered 3D flags with different textures and flags cut out from real images. The synthetic data showed that it can achieve an accuracy above 80% compared to 88% accuracy achieved by a data set containing only real images. The highest accuracy scored was achieved by combining real and synthetic data showing that synthetic data can be used as a complement to real data. Some attempts to improve the ac
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Oquab, Maxime. "Convolutional neural networks : towards less supervision for visual recognition." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE061.

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Les réseaux de neurones à convolution sont des algorithmes d’apprentissage flexibles qui tirent efficacement parti des importantes masses de données qui leur sont fournies pour l’entraînement. Malgré leur utilisation dans des applications industrielles dès les années 90, ces algorithmes n’ont pas été utilisés pour la reconnaissance d’image à cause de leurs faibles performances avec les images naturelles. C’est finalement grâce a l’apparition d’importantes quantités de données et de puissance de calcul que ces algorithmes ont pu révéler leur réel potentiel lors de la compétition ImageNet, menan
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Yedroudj, Mehdi. "Steganalysis and steganography by deep learning." Thesis, Montpellier, 2019. http://www.theses.fr/2019MONTS095.

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La stéganographie d'image est l'art de la communication secrète dans le but d'échanger un message de manière furtive. La stéganalyse d'image a elle pour objectif de détecter la présence d'un message caché en recherchant les artefacts présent dans l'image. Pendant une dizaine d'années, l'approche classique en stéganalyse a été d'utiliser un ensemble classifieur alimenté par des caractéristiques extraites "à la main". Au cours des dernières années, plusieurs études ont montré que les réseaux de neurones convolutionnels peuvent atteindre des performances supérieures à celles des approches convent
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Staccone, Francesco. "Deep Learning for Sea-Ice Classification on Synthetic Aperture Radar (SAR) Images in Earth Observation : Classification Using Semi-Supervised Generative Adversarial Networks on Partially Labeled Data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-277920.

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Earth Observation is the gathering of information about planet Earth’s system via Remote Sensing technologies for monitoring land cover types and their changes. Through the years, image classification techniques have been widely studied and employed to extract useful information from Earth Observation data such as satellite imagery. One of the most attractive use cases is the monitoring of polar regions, that recently observed some dramatic changes due to global warming. Indeed drifting ice caps and icebergs represent threats to ship activities and navigation in polar areas, and the risk of co
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Tirumaladasu, Sai Subhakar, and Shirdi Manjunath Adigarla. "Autonomous Driving: Traffic Sign Classification." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17783.

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Autonomous Driving and Advance Driver Assistance Systems (ADAS) are revolutionizing the way we drive and the future of mobility. Among ADAS, Traffic Sign Classification is an important technique which assists the driver to easily interpret traffic signs on the road. In this thesis, we used the powerful combination of Image Processing and Deep Learning to pre-process and classify the traffic signs. Recent studies in Deep Learning show us how good a Convolutional Neural Network (CNN) is for image classification and there are several state-of-the-art models with classification accuracies over 99
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Chowdhury, Muhammad Iqbal Hasan. "Question-answering on image/video content." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/205096/1/Muhammad%20Iqbal%20Hasan_Chowdhury_Thesis.pdf.

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This thesis explores a computer's ability to understand multimodal data where the correspondence between image/video content and natural language text are utilised to answer open-ended natural language questions through question-answering tasks. Static image data consisting of both indoor and outdoor scenes, where complex textual questions are arbitrarily posed to a machine to generate correct answers, was examined. Dynamic videos consisting of both single-camera and multi-camera settings for the exploration of more challenging and unconstrained question-answering tasks were also considered. I
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MAGGIOLO, LUCA. "Deep Learning and Advanced Statistical Methods for Domain Adaptation and Classification of Remote Sensing Images". Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1070050.

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In the recent years, remote sensing has faced a huge evolution. The constantly growing availability of remote sensing data has opened up new opportunities and laid the foundations for many new challenges. The continuous space missions and new constellations of satellites allow in fact more and more frequent acquisitions, at increasingly higher spatial resolutions, and at an almost total coverage of the globe. The availability of such an huge amount data has highlighted the need for automatic techniques capable of processing the data and exploiting all the available information. Meanwhile, the
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Books on the topic "Deep Convolutional Generative Adversarial Networks (DCGAN)"

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Sangeetha, V., and S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.

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Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip it
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Book chapters on the topic "Deep Convolutional Generative Adversarial Networks (DCGAN)"

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Ghayoumi, Mehdi. "Deep Convolutional Generative Adversarial Networks (DCGANs)." In Generative Adversarial Networks in Practice. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003281344-8.

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Ou, Xunxiong. "Deep Convolutional Generative Adversarial Networks (DCGAN)-Based Anime Face Generation." In Advances in Computer Science Research. Atlantis Press International BV, 2024. http://dx.doi.org/10.2991/978-94-6463-540-9_86.

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Ghosh, Bravish, and Manoranjan Parhi. "Lightweight Model for Waifu Creation Using Deep Convolutional Generative Adversarial Network (DCGAN)." In Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2225-1_11.

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kaushik, Charu, and Shailendra Narayan Singh. "Generate Artificial Human Faces with Deep Convolutional Generative Adversarial Network (DCGAN) Machine Learning Model." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5974-7_5.

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Juefei-Xu, Felix, Eshan Verma, and Marios Savvides. "DeepGender2: A Generative Approach Toward Occlusion and Low-Resolution Robust Facial Gender Classification via Progressively Trained Attention Shift Convolutional Neural Networks (PTAS-CNN) and Deep Convolutional Generative Adversarial Networks (DCGAN)." In Deep Learning for Biometrics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61657-5_8.

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Aryan, Abhishek, Vignesh Kashyap, and Anurag Goel. "Image Generation Using Deep Convolutional Generative Adversarial Networks." In Research Advances in Intelligent Computing. CRC Press, 2023. http://dx.doi.org/10.1201/9781003320340-21.

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Bathla, Astha, and Aakanshi Gupta. "Image Formation Using Deep Convolutional Generative Adversarial Networks." In Predictive Analytics. CRC Press, 2020. http://dx.doi.org/10.1201/9781003083177-5.

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Esan, Dorcas Oladayo, Pius Adewale Owolawi, and Chunling Tu. "Artistic Image Generation Using Deep Convolutional Generative Adversarial Networks." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71079-7_1.

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Jasmine, S., Tina Esther Trueman, P. Narayanasamy, and J. Ashok Kumar. "Finger Vein Identification Using Deep Convolutional Generative Adversarial Networks." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2674-6_13.

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Aslan, Süleyman, Uğur Güdükbay, B. Uğur Töreyin, and A. Enis Çetin. "Deep Convolutional Generative Adversarial Networks for Flame Detection in Video." In Computational Collective Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63007-2_63.

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Conference papers on the topic "Deep Convolutional Generative Adversarial Networks (DCGAN)"

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G, Gowthami, Mano Shankari J, and Tina Babu. "Retinal Scan Denoising Using Generative Adversarial Networks: A Deep Convolutional Approach." In 2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS). IEEE, 2024. https://doi.org/10.1109/icicnis64247.2024.10823330.

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Anvitha, Sagala Sai, Sagam Prashamsa Reddy, Yerramachu Sunaini, and Nidhin Prabhakar T. V. "Generation of Photorealistic Face Images Using Deep Convolutional Generative Adversarial Networks." In 2024 IEEE 8th International Conference on Information and Communication Technology (CICT). IEEE, 2024. https://doi.org/10.1109/cict64037.2024.10899610.

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D, Prabakaran, Praveena N. G, Samuda Prathima, Beulah Jackson, Uma Maheswari, and Srigitha S. Nath. "Novel Hybrid Deep Convolutional and Cycle Generative Adversarial Networks for efficient Image Restoration." In 2024 International Conference on Advances in Computing, Communication and Materials (ICACCM). IEEE, 2024. https://doi.org/10.1109/icaccm61117.2024.11059079.

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KEMMOU, Abdelaali, Adil EL MAKRANI, Ikram ELAZAMI, Fouad LEHLOU, and Moulay Hafid AABIDI. "Improved Facial Expression Recognition Through Occluded Optical Flow Reconstruction Using Deep Convolutional Generative Adversarial Network." In 2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM). IEEE, 2024. http://dx.doi.org/10.1109/wincom62286.2024.10657959.

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Yoganand, Yoshitha, Gomathi G. Bagewadi, Karishma S. Teckani, and Balachandra A. "Securing IoT Data Transmission with Deep Learning Approach to Steganography with Convolutional Neural Networks and Generative Adversarial Networks." In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS). IEEE, 2024. https://doi.org/10.1109/iciics63763.2024.10859948.

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Upadhyay, Deepak, Abhay Upadhyay, Kuj Bihari Sharma, Shiv Ashish Dhondiyal, and Nookala Venu. "Exploring the Impact of Conditional Deep Convolutional Generative Adversarial Networks in Brain Tumor Image Classification: A Novel Approach." In 2025 International Conference on Pervasive Computational Technologies (ICPCT). IEEE, 2025. https://doi.org/10.1109/icpct64145.2025.10940581.

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Yar, Ghulam Nabi Ahmad Hassan, Muhammad Taha, Zeshan Afzal, Farhan Zafar, Inam-Ur-Rehman Shahid, and Abubakar Noor-Ul-Hassan. "TexGAN: Textile Pattern Generation Using Deep Convolutional Generative Adversarial Network (DCGAN)." In 2023 IEEE International Conference on Emerging Trends in Engineering, Sciences and Technology (ICES&T). IEEE, 2023. http://dx.doi.org/10.1109/icest56843.2023.10138848.

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Nasrin, Sayeda Samia, and Risul Islam Rasel. "HennaGAN: Henna Art Design Generation using Deep Convolutional Generative Adversarial Network (DCGAN)." In 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE). IEEE, 2020. http://dx.doi.org/10.1109/wiecon-ece52138.2020.9398005.

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Viola, Jairo, YangQuan Chen, and Jing Wang. "FaultFace: Deep Convolutional Generative Adversarial Network (DCGAN) based Ball-Bearing Failure Detection Method." In 2019 1st International Conference on Industrial Artificial Intelligence (IAI). IEEE, 2019. http://dx.doi.org/10.1109/iciai.2019.8850805.

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Assis, Érika, Mark Song, Luis Zárate, and Cristiane Nobre. "Data Balancing using Deep Convolutional Generative Adversarial Networks (DCGAN) in Patients with Congenital Syndrome by Zika Virus." In 15th International Conference on Health Informatics. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010842900003123.

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