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pro vyhledávání: '"Basha, S. H. Shabbeer"'
Transfer Learning enables Convolutional Neural Networks (CNN) to acquire knowledge from a source domain and transfer it to a target domain, where collecting large-scale annotated examples is time-consuming and expensive. Conventionally, while transfe
Externí odkaz:
http://arxiv.org/abs/2205.05967
The convolutional neural networks (CNNs) are generally trained using stochastic gradient descent (SGD) based optimization techniques. The existing SGD optimizers generally suffer with the overshooting of the minimum and oscillation near minimum. In t
Externí odkaz:
http://arxiv.org/abs/2109.12504
Autor:
Basha, S. H. Shabbeer, Farazuddin, Mohammad, Pulabaigari, Viswanath, Dubey, Shiv Ram, Mukherjee, Snehasis
Deep Convolutional Neural Networks (DCNNs) have shown promising performances in several visual recognition problems which motivated the researchers to propose popular architectures such as LeNet, AlexNet, VGGNet, ResNet, and many more. These architec
Externí odkaz:
http://arxiv.org/abs/2102.00160
Autor:
Basha, S. H. Shabbeer, Vinakota, Sravan Kumar, Pulabaigari, Viswanath, Mukherjee, Snehasis, Dubey, Shiv Ram
Transfer learning enables solving a specific task having limited data by using the pre-trained deep networks trained on large-scale datasets. Typically, while transferring the learned knowledge from source task to the target task, the last few layers
Externí odkaz:
http://arxiv.org/abs/2005.02165
We propose a novel scheme for human action recognition in videos, using a 3-dimensional Convolutional Neural Network (3D CNN) based classifier. Traditionally in deep learning based human activity recognition approaches, either a few random frames or
Externí odkaz:
http://arxiv.org/abs/2002.02100
Autor:
Basha, S. H. Shabbeer, Vinakota, Sravan Kumar, Dubey, Shiv Ram, Pulabaigari, Viswanath, Mukherjee, Snehasis
Deep Convolutional Neural Networks (CNN) have evolved as popular machine learning models for image classification during the past few years, due to their ability to learn the problem-specific features directly from the input images. The success of de
Externí odkaz:
http://arxiv.org/abs/2001.11951
The Convolutional Neural Networks (CNNs), in domains like computer vision, mostly reduced the need for handcrafted features due to its ability to learn the problem-specific features from the raw input data. However, the selection of dataset-specific
Externí odkaz:
http://arxiv.org/abs/1902.02771
Autor:
Basha, S H Shabbeer, Ghosh, Soumen, Babu, Kancharagunta Kishan, Dubey, Shiv Ram, Pulabaigari, Viswanath, Mukherjee, Snehasis
Efficient and precise classification of histological cell nuclei is of utmost importance due to its potential applications in the field of medical image analysis. It would facilitate the medical practitioners to better understand and explore various
Externí odkaz:
http://arxiv.org/abs/1810.02797
Publikováno v:
Neural Computing & Applications; Nov2024, Vol. 36 Issue 33, p20607-20616, 10p
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