Research on structure and defense of adversarial example in deep learning by Guanghan DUAN, Chunguang MA, Lei SONG, Peng WU

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Machine and deep learning performance in out-of-distribution regressions by Assaf Shmuel, Oren Glickman, Teddy Lazebnik

“…Machine learning (ML) and deep learning (DL) models are gaining popularity due to their effectiveness in many computational tasks. …”
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Survey on intellectual property protection for deep learning model by Xinya WANG, Guang HUA, Hao JIANG, Haijian ZHANG

“…With the rapid development of deep learning technology, deep learning models have been widely used in many fields such as image classification and speech recognition.Training a deep learning model relies on a large amount of data and computing power, thus selling the trained model or providing specific services (DLaaS, e.g.) has become a new business.However, the commercial interests of model trainers and the intellectual property rights of model developers may be violated if the model is maliciously stolen.With deep neural network watermarking becoming a new research topic, multimedia copyright protection techniques were used for deep learning model protection.Numerous methods have been proposed in this field and then a comprehensive survey is needed.the existing deep neural network watermarking methods were elaborated and summarized and the future research directions of this field were discussed.The overall framework of neural network watermarking was presented, whereby the basic concepts such as classification model and model backdoor were introduced.Secondly, the existing methods were divided into two types according to the mechanism of watermark embedding, one is to embed the watermark bits into the carrier of internal information of the network, and the other one uses the established backdoor mapping as the watermark.These two existing deep neural network watermarking methods were analyzed and summarized, and attacks to the watermarks were also introduced and discussed.By analyzing the white-box and black-box conditions in watermarking scenario, it comes to the conclusion that the model is difficult to be effectively protected when it is distributed in the white-box manner, and the neural network watermark defenses in the black-box distribution and black-box verification are both worthy for further research.…”
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Global Geomagnetic Perturbation Forecasting Using Deep Learning by Vishal Upendran, Panagiotis Tigas, Banafsheh Ferdousi, Téo Bloch, Mark C. M. Cheung, Siddha Ganju, Asti Bhatt, Ryan M. McGranaghan, Yarin Gal

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Channel estimation for OFDM system based on deep learning by Yun ZHANG, Jing ZHOU, Jingwei HUANG, Shujuan YU, Liya HUANG

Subjects: “…deep learning…”
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Applications of Deep Learning to MRI Images: A Survey by Jin Liu, Yi Pan, Min Li, Ziyue Chen, Lu Tang, Chengqian Lu, Jianxin Wang

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Deep Learning-Based Damage Assessment in Cherry Leaves by Burakhan Cubukcu, Hazel BOZCU

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A convergent Deep Learning algorithm for approximation of polynomials by Després, Bruno

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Honeypot contract detection of blockchain based on deep learning by Hongxia ZHANG, Qi WANG, Dengyue WANG, Ben WANG

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Radio signal recognition based on image deep learning by Xin ZHOU, Xiaoxin HE, Changwen ZHENG

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Research on mimic decision method based on deep learning by Xiaohan YANG, Guozhen CHENG, Wenyan LIU, Shuai ZHANG, Bing HAO

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The application of deep learning in early enamel demineralization detection by Ketai He, Rongxiu Zhang, Muchun Liang, Keyue Tian, Kaihui Luo, Ruoshi Chen, Jianpeng Ren, Jiajun Wang, Juan Li

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SYMBOLIC ANALYSIS OF CLASSICAL NEURAL NETWORKS FOR DEEP LEARNING by Vladimir Milićević, Igor Franc, Maja Lutovac Banduka, Nemanja Zdravković, Nikola Dimitrijević

“…Deep learning is usually based on matrix computing with a large number of hidden parameters that are not visible outside the computing module. …”
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Advanced deep learning techniques for recognition of dental implants by Veena Benakatti, Ramesh P. Nayakar, Mallikarjun Anandhalli, Rohit sukhasare

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A survey of deep learning based modulation recognition by Shujun SUN, Shengliang PENG, Yudong YAO, Xi YANG

Subjects: “…deep learning…”
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Cleanup Sketched Drawings: Deep Learning-Based Model by Amal Ahmed Hasan Mohammed, Jiazhou Chen

“…This paper demonstrates the efficiency and effectiveness of using deep learning in cleaning and improving the roughly drawn image in an automatic way. …”
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Introduction to deep learning methods for multi‐species predictions by Yuqing Hu, Sara Si‐Moussi, Wilfried Thuiller

“…Additionally, we showcased how interpretability tools can illuminate community structure as seen by deep learning models. Deep learning models offer a broad array of features for predicting entire species communities. …”
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An overview of skin cancer classification based on deep learning by Toqa A. Sadoon

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Deep Learning-Based Dzongkha Handwritten Digit Classification by Yonten Jamtsho, Pema Yangden, Sonam Wangmo, Nima Dema

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A Scheme Based on Deep Learning for Fruit Classification by Ali Orangzeb Panhwar, Anwar Ali Sathio, Nadeem Manzoor Shah, Sumaira Memon

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