Filters








64 Hits in 6.6 sec

An Entropic Optimal Transport Loss for Learning Deep Neural Networks under Label Noise in Remote Sensing Images [article]

Bharath Bhushan Damodaran, Rémi Flamary, Viven Seguy, Nicolas Courty
2018 arXiv   pre-print
It allows to learn in an end-to-end fashion deep neural networks that are, to some extent, robust to inaccurately labeled samples.  ...  To mitigate this effect, we propose an original solution with entropic optimal transportation.  ...  Paolo Gamba for providing Pavia University dataset, and the National Center for Airborne Laser Mapping and the Hyperspectral Image Analysis Laboratory at the University of Houston for acquiring and providing  ... 
arXiv:1810.01163v1 fatcat:bfyiw4wksjhytegxvrpmnkunl4

Wasserstein Adversarial Regularization (WAR) on label noise [article]

Kilian Fatras, Bharath Bhushan Damodaran, Sylvain Lobry, Rémi Flamary, Devis Tuia, Nicolas Courty
2021 arXiv   pre-print
We first discuss how and why adversarial regularization can be used in the context of label noise and then show the effectiveness of our method on five datasets corrupted with noisy labels: in both benchmarks  ...  We propose a new regularization method, which enables learning robust classifiers in presence of noisy data.  ...  As pointed out in [4] , deep convolutional neural networks have huge memorization abilities and can learn very complex functions.  ... 
arXiv:1904.03936v3 fatcat:pmllglritvanhbudvjzfbzozeq

Image Compression Using Deep Learning: Methods and Techniques

Arwa Sahib Abd-Alzhra, Mohammed S. H. Al- Tamimi
2022 Iraqi Journal of Science  
Several neural networks and deep learning methods have been used to compress images.  ...  As a result, it is hard to find and recover a well-compressed representation for images, and it also hard to design and test networks that are able to recover images successfully in a lossless or lossy  ...  Alexandre et al. 2019 [37] suggested a compression method for loss-based images using deep-learning AutoEncoder structure and provided an AutoEncoder -based learned image compressor with the notion of  ... 
doi:10.24996/ijs.2022.63.3.34 fatcat:43hnfu33krahrarvnab4ul3rq4

Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing [article]

Vishal Monga, Yuelong Li, Yonina C. Eldar
2020 arXiv   pre-print
Deep neural networks provide unprecedented performance gains in many real world problems in signal and image processing.  ...  in signal processing and deep neural networks.  ...  Multi-spectral image fusion is a fundamental problem in remote sensing. Lohit et al.  ... 
arXiv:1912.10557v3 fatcat:klkwcacburca3hr63m7v77pvnq

Procedural Learning with Robust Visual Features via Low Rank Prior

Haifeng Li, Li Chen, Hailun ding, Qi Li, Bingyu Sun, Guohua Wu
2019 IEEE Access  
In order to apply a convolutional neural network (CNN) to unseen datasets, a common way is to train a CNN using a pre-trained model on a big dataset by fine-tuning it instead of starting from scratch.  ...  In this framework, we presented an approach to yield independent visualization features (IVFs).  ...  INTRODUCTION Convolutional neural network (CNN) has achieved remarkable successes in computer vision, remote sensing image recognition tasks [1] , [2] .  ... 
doi:10.1109/access.2019.2894841 fatcat:xhdr4jnrhrgm3n77t22l6vojpi

A Survey on Multimedia Services QoE Assessment and Machine Learning Based Prediction

Georgios Kougioumtzidis, Vladimir Poulkov, Zaharias Zaharis, Pavlos Lazaridis
2022 IEEE Access  
In this regard, a paradigm shift in network implementations is being envisioned, in which the focus will be on machine learning (ML) methodologies for developing QoE prediction models, directly related  ...  In this survey, an analysis on application-oriented, ML-based QoE prediction models for the goal of QoE management for multimedia services is presented.  ...  X ARTIFICIAL NEURAL NETWORKS ALGORITHMS Method Learning type Description Multilayer Supervised, Data modeling using simple perceptron unsupervised, correlations reinforcement Deep neural Supervised, Modeling  ... 
doi:10.1109/access.2022.3149592 fatcat:jw32khnuu5el5oef4vvh5x3jl4

A Metaverse: taxonomy, components, applications, and open challenges

Sang-Min Park, Young-Gab Kim
2022 IEEE Access  
With the technological development of deep learning-based high-precision recognition models and natural generation models, Metaverse is being strengthened with various factors, from mobile-based always-on  ...  The integration of enhanced social activities and neural-net methods requires a new definition of Metaverse suitable for the present, different from the previous Metaverse.  ...  It optimizes the weighted-contrast learning loss and lowers its contribution to the overall loss. Morgado et al.  ... 
doi:10.1109/access.2021.3140175 fatcat:fnraeaz74vh33knfvhzrynesli

A Survey on Analyzing Encrypted Network Traffic of Mobile Devices [article]

Ashutosh Bhatiaa, Ankit AgrawalaAyush Bahugunaa, Kamlesh Tiwaria, K. Haribabua, Deepak Vishwakarmab
2020 arXiv   pre-print
optimization, etc.  ...  Beyond traditional use for communication, they are used for many peripheral tasks such as gaming, browsing, and shopping.  ...  This research was supported by the Center for Artificial Intelligence and Robotics (CAIR) lab of Defence Research and Development Organisation (DRDO), India, Bangalore under the CARS scheme.  ... 
arXiv:2006.12352v1 fatcat:cysjaqpqdfbxjn7b2gsy6gyelu

Partial Wasserstein Adversarial Network for Non-rigid Point Set Registration [article]

Zi-Ming Wang, Nan Xue, Ling Lei, Gui-Song Xia
2022 arXiv   pre-print
In addition, it also incorporates an efficient coherence regularizer for non-rigid transformations to avoid unrealistic deformations.  ...  Based on these results, we propose a partial Wasserstein adversarial network (PWAN), which is able to approximate the PW discrepancy by a neural network, and minimize it by gradient descent.  ...  To estimate the KR forms, we parametrize the potential function f w,h by a neural network shown in Fig. 10, and learn f w,h by maximizing (10) or (11).  ... 
arXiv:2203.02227v1 fatcat:deiazqyctrc23hrv56lyb23j4a

Consciousness in Neural Networks?

Edmund T Rolls
1997 Neural Networks  
The nature of the neural systems needed for the creation of awareness is reviewed, leading to a set of characteristics required for the crucial experiments able to uncover the processes involved.  ...  Consciousness and neural cognizers: a review of some recent approaches [Abstract] [Full text] (PDF 441.4 Kb) 1303-1316 Ron Sun Learning, action and consciousness: a hybrid approach toward modelling consciousness  ...  It provided materials for Chapter 2 in ''Societies of Brains'' (1995), and is given here with the permission of the publisher, Lawrence Erlbaum Associates, Hillsdale, NJ.  ... 
doi:10.1016/s0893-6080(97)00049-x pmid:12662513 fatcat:q3rt2km7snbmlcbpkw6lfac3gq

Indirect Image Registration with Large Diffeomorphic Deformations

Chong Chen, Ozan Öktem
2018 SIAM Journal of Imaging Sciences  
The paper concludes with examples of indirect image registration in 2D tomography with very sparse and/or highly noisy data.  ...  The paper adapts the large deformation diffeomorphic metric mapping framework for image registration to the indirect setting where a template is registered against a target that is given through indirect  ...  We especially thank Barbara Gris for contributions in stating and proving many of the results in section 7.  ... 
doi:10.1137/17m1134627 fatcat:pncuaoa4jnelrcczeuwzgymxvy

Abstracts of the 16th Annual Meeting of The Israel Society for Neuroscience: Eilat, Israel, November 25–27, 2007

2007 Neural Plasticity  
The Israel Society for Neuroscience—ISFN—was founded in 1993 by a group of Israeli leading scientists conducting research in the area of neurobiology.  ...  Hotel in Eilat, Israel.Further information concerning the Israel Society for Neuroscience can be found at http://www.isfn.org.il.  ...  The effects of music background on sleep efficiency, anxiety and depression in Schizophrenia patients  ... 
doi:10.1155/2007/30585 pmid:25148070 pmcid:PMC2366074 fatcat:u4tjfw6shnaxpnzwezieeiqnpm

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

Alcardo Alex Barakabitze, Nabajeet Barman, Arslan Ahmad, Saman Zadtootaghaj, Lingfen Sun, Maria G. Martini, Luigi Atzori
2019 IEEE Communications Surveys and Tutorials  
issues in QoE optimization.  ...  service management in softwarized networks.  ...  More intelligence in the neural network reasoner can be added by incorporating online learning behaviours such as Q-Learning.  ... 
doi:10.1109/comst.2019.2958784 fatcat:7bgzl5rpmfgedo5e5psz7i3t4a

Situational Understanding in the Human and the Machine

Yan Yufik, Raj Malhotra
2021 Frontiers in Systems Neuroscience  
In the last half century, advances in AI have been concentrated in the area of machine learning.  ...  Suggestions for further R&D are motivated by these hypotheses and are centered on the notions of Active Inference and Virtual Associative Networks.  ...  “Deep neural networks are easily fooled: high confidence predictions for unrecognizable images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , Boston, MA, 427  ... 
doi:10.3389/fnsys.2021.786252 pmid:35002643 pmcid:PMC8733725 fatcat:zeamg7i7rbc6rgh5ev7ffamx5a

第57回年会予稿集

2019 Seibutsu Butsuri  
Using these data, we have developed methods for prediction of protein contacts and protein-ligand interactions based on deep neural networks.  ...  -S323 -Poster, Day 2 2Pos067 Deep convolutional neural networks for identifying cryo-EM grid holes suitable for particle collection Yuichi Yokoyama 1 , Tohru Terada 3 , Kentaro Shimizu 2,3 , Kazutoshi  ...  Kinesin is a motor protein that uses microtubules as pathways for intracellular transport.  ... 
doi:10.2142/biophys.59.s1 fatcat:rsudhlmaanetrhmdxjgsiyorme
« Previous Showing results 1 — 15 out of 64 results