Results 271 to 280 of about 52,085 (312)

Multi-task advanced convolutional neural network for robust lymphoblastic leukemia diagnosis, classification, and segmentation. [PDF]

PeerJ Comput Sci
Yalcin S, Cetin Yalcin Z, Yildirim M, Alatas B.   +3 more
europepmc   +1 more source

Review of the Current State of Artificial Intelligence in Pediatric Cardiovascular Magnetic Resonance Imaging. [PDF]

Children (Basel)
Gearhart A, Anjewierden S, Buddhe S, Tandon A.   +3 more
europepmc   +1 more source

Hybrid Deep Learning Model Integrating Long Short-Term Memory Networks and Convolutional Neural Nets for Predicting Anesthesia Outcomes in Surgical Procedures

P. Nithiyasree, Kavitha Subramani, V Sathiya, M. Maheswari, M.S. Vinmathi, S. Balaji   +5 more
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SRGC-Nets: Sparse Repeated Group Convolutional Neural Networks

IEEE Transactions on Neural Networks and Learning Systems, 2020
Group convolution is widely used in many mobile networks to remove the filter's redundancy from the channel extent. In order to further reduce the redundancy of group convolution, this article proposes a novel repeated group convolutional (RGC) kernel, which has M primary groups, and each primary group includes N tiny groups.
Yao Lu, Guangming Lu, Rui Lin, Jinxing Li, David Zhang   +4 more
openaire   +2 more sources

KNOWLEDGE TRANSFER IN DEEP CONVOLUTIONAL NEURAL NETS

International Journal on Artificial Intelligence Tools, 2008
Knowledge transfer is widely held to be a primary mechanism that enables humans to quickly learn new complex concepts when given only small training sets. In this paper, we apply knowledge transfer to deep convolutional neural nets, which we argue are particularly well suited for knowledge transfer. Our initial results demonstrate that components of a
STEVEN GUTSTEIN, OLAC FUENTES, ERIC FREUDENTHAL   +2 more
openaire   +1 more source

Recognizing objectionable images using convolutional neural nets

2015 Signal Processing and Intelligent Systems Conference (SPIS), 2015
In recent years different methods for detecting objectionable images have proposed. All of the previous systems are based on extracting pre-defined and certain features from the images. In this paper a method is proposed in order to detect objectionable images using convolutional neural networks.
Reza Moradi, Rahman Yousefzadeh
openaire   +1 more source

Mobile Net Convolutional Neural Networks for Video Classification

2021
Current data and correspondence advances give the foundation to send bits anyplace, however don't dare to deal with data at the semantic level.s This paper researches the utilization of video content investigation and high- light extraction and bunching strategies for additional video semantic arrangements.
Sudhakar Putheti, Bhimavarapu Sravya Pranati, Vishwaksen Bairisetti   +2 more
openaire   +1 more source

Pulse-Net: Dynamic Compression of Convolutional Neural Networks

2019 IEEE 5th World Forum on Internet of Things (WF-IoT), 2019
Convolutional Neural Networks (CNNs) are used in a range of computer vision tasks, with state-of-the-art CNNs such as AlexNet and VGG16 constructed using a large number of parameter and multiply-add operations (MACs). These tasks require high computational power and high energy requirements to run the CNNs, making them unsuitable for deployment on In ...
Browne Browne, Giering Giering, Prestwich Prestwich   +2 more
openaire   +1 more source

Convolutional neural net bagging for online visual tracking

Computer Vision and Image Understanding, 2016
The proposed CNN bagging method is simple yet effective.It addresses the label noise and model uncertainty problems simultaneously for CNN-based trackers.The state-of-the-art performances on 3 recent benchmarks i.e., CVPR2013, VOT2013 and TB50 illustrate the validity of the proposed algorithm.
Hanxi Li, Yi Li, Fatih Porikli
openaire   +1 more source