Results 11 to 20 of about 117,293 (275)

Poincaré ResNet

2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023
International Conference on Computer Vision ...
van Spengler, M., Berkhout, E., Mettes, P.   +2 more
openaire   +3 more sources

Comparative Analysis of Transfer Learning-Based CNN Approaches for Recognition of Traffic Signs in Autonomous Vehicles [PDF]

E3S Web of Conferences, 2023
Traffic signs recognition has a crucial role in enhancing the safety and efficienty of autonomous vehicles (AVs). This AVs can contribute to a cleaner and healthier environment by improving fuel efficiency, minimizing travel distances, and deacreasing ...
Fatima Ezzahra Khalloufi, Najat Rafalia, Jaafar Abouchabaka   +2 more
doaj   +1 more source

Digital Soil Mapping of Soil Organic Matter with Deep Learning Algorithms

ISPRS International Journal of Geo-Information, 2022
Digital soil mapping has emerged as a new method to describe the spatial distribution of soils economically and efficiently. In this study, a lightweight soil organic matter (SOM) mapping method based on a deep residual network, which we call LSM-ResNet,
Pengyuan Zeng, Xuan Song, Huan Yang, Ning Wei, Liping Du   +4 more
doaj   +1 more source

Deep Learning Techniques for Quantification of Tumour Necrosis in Post-neoadjuvant Chemotherapy Osteosarcoma Resection Specimens for Effective Treatment Planning

Acta Informatica Pragensia, 2023
Osteosarcoma is a high-grade malignant bone tumour for which neoadjuvant chemotherapy is a vital component of the treatment plan. Chemotherapy brings about the death of tumour tissues, and the rate of their death is an essential factor in deciding on ...
T. S. Saleena, P. Muhamed Ilyas, V. M. Kutty Sajna, A. K. M. Bahalul Haque   +3 more
doaj   +1 more source

Comparative Analysis of Deep Learning Models Used in Impact Analysis of Coronavirus Chest X-ray Imaging

Biomedicines, 2022
The impact analysis of deep learning models for COVID-19-infected X-ray images is an extremely challenging task. Every model has unique capabilities that can provide suitable solutions for some given problem.
Musiri Kailasanathan Nallakaruppan, Subhashini Ramalingam, Siva Rama Krishnan Somayaji, Sahaya Beni Prathiba   +3 more
doaj   +1 more source

Attention-Based Residual Network with Scattering Transform Features for Hyperspectral Unmixing with Limited Training Samples

Remote Sensing, 2020
This paper proposes a framework for unmixing of hyperspectral data that is based on utilizing the scattering transform to extract deep features that are then used within a neural network.
Yiliang Zeng, Christian Ritz, Jiahong Zhao, Jinhui Lan   +3 more
doaj   +1 more source

Neural Models for Target-Based Computer-Assisted Musical Orchestration: A Preliminary Study

Journal of Creative Music Systems, 2022
In this paper we will perform a preliminary exploration on how neural networks can be used for the task of target-based computer-assisted musical orchestration.
Carmine Emanuele Cella, Luke Jonathan Dzwonczyk, Alejandro Saldarriaga-Fuertes, Hongfu Liu, Hélène-Camille Crayencour   +4 more
doaj   +1 more source

Cross-View Gait Recognition Method Based on Multi-Teacher Joint Knowledge Distillation

Sensors, 2023
Aiming at challenges such as the high complexity of the network model, the large number of parameters, and the slow speed of training and testing in cross-view gait recognition, this paper proposes a solution: Multi-teacher Joint Knowledge Distillation ...
Ruoyu Li, Lijun Yun, Mingxuan Zhang, Yanchen Yang, Feiyan Cheng   +4 more
doaj   +1 more source

SPEAKER IDENTIFICATION SYSTEM USING AUDIO SIGNAL AND DEEP LEARNING METHOD [PDF]

Proceedings on Engineering Sciences
Automatic Speaker Identification (ASI) does not result in high accuracy, so it is essential to develop a highly accurate Speaker Identification (SI) system. Artificial Intelligence has shown remarkable improvement in the development of such systems using
Neelam Nehra , Pardeep Sangwan , Neelu Trivedi   +2 more
doaj   +1 more source

Why ResNet Works? Residuals Generalize [PDF]

IEEE Transactions on Neural Networks and Learning Systems, 2020
Residual connections significantly boost the performance of deep neural networks. However, there are few theoretical results that address the influence of residuals on the hypothesis complexity and the generalization ability of deep neural networks.
Fengxiang He, Tongliang Liu, Dacheng Tao
openaire   +3 more sources