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Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
[article]
2020
arXiv
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We consider the problem of detecting out-of-distribution images in neural networks. We propose ODIN, a simple and effective method that does not require any change to a pre-trained neural network. ...
Our method is based on the observation that using temperature scaling and adding small perturbations to the input can separate the softmax score distributions between in- and out-of-distribution images ...
ACKNOWLEDGMENTS The research reported here was supported by NSF Grant CPS ECCS 1739189. ...
arXiv:1706.02690v5
fatcat:fmhiufcazbf6vm6c4qibgjwl6y
An Efficient Data Augmentation Network for Out-of-Distribution Image Detection
2021
IEEE Access
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Therefore, out-of-distribution detection (also called anomaly detection or outlier detection) of image classification has become a critical issue for the successful development of neural networks. ...
INDEX TERMS Out-of-distribution detection, image classification, anomaly detection, outlier detection, data augmentation, deep neural networks. ...
ACKNOWLEDGMENT The authors are grateful to the National Center for High-Performance Computing for computer time and facilities to conduct this research. ...
doi:10.1109/access.2021.3062187
fatcat:2mgianj3ijdfvbfyssqecaqolu
Survey on Neural Networks Used for Medical Image Processing
2009
International Journal of Computational Science
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This paper aims to present a review of neural networks used in medical image processing. We classify neural networks by its processing goals and the nature of medical images. ...
By this survey, we try to answer the following two important questions: (1) What are the major applications of neural networks in medical image processing now and in the nearby future? ...
Acknowledgements The authors are grateful to the anonymous referees for their constructive and helpful comments. ...
pmid:26740861
pmcid:PMC4699299
fatcat:4scj7gtidva73k3xzls346punq
Parallel Image Processing Using Algorithmic Skeletons
2014
International Journal of Intelligent Information Systems
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In the last few decades, image processing has achieved significant theoretical and practical progress. ...
The present paper aims to present a technique for image processing which utilizes design and analysis of parallel algorithms. ...
Neural network: The neural network for recognizing the objects which are detected in the previous part. ...
doi:10.11648/j.ijiis.s.2014030601.12
fatcat:u6jihghwjvbmlb6znoidss775i
Detection of lung cancer using image processing techniques
2016
International Journal of Advanced Technology and Engineering Exploration
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These features are normalized in the range -1 to 1 and given as an input to the artificial neural network for detection. ...
The work carried out involves processing of MRI images that are affected by cancer for detection and providing medicines for cure. ...
The system uses computer based procedures to detect tumor blocks or lesions and classify the type of tumor using Artificial Neural Network in MRI images of different patients with Astrocytoma type of tumors ...
doi:10.19101/ijatee.2016.325004
fatcat:jehjlyyggvffvmemrwvmelnwwy
Design of Shallow Neural Network Based Plant Disease Detection System
2021
European Journal of Electrical Engineering and Computer Science
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— In this work, we proposed the use of a shallow neural network for plant disease detection. ...
The overall detection accuracy of the model is 98.25%. ...
In majority of these listed studies, the shallow neural network was used as opposed the deep neural network. ...
doi:10.24018/ejece.2021.5.4.337
fatcat:44mvdavlunfbpibiegcrrbyitq
A New CAD System for Breast Microcalcifications Diagnosis
2016
International Journal of Advanced Computer Science and Applications
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The second step of the proposed CAD system consists on segmenting microcalcifications clusters, using Generalized Gaussian Density (GGD) estimation and a Bayesian backpropagation neural network. ...
Breast cancer is one of the most deadly cancers in the world, especially among women. ...
A wavelet contrast enhancement step is then carried out followed by a detection and suppression of pectoral muscle. ...
doi:10.14569/ijacsa.2016.070417
fatcat:bwwmnntdizh5poc5y5whfm2ziy
FLAME AND SMOKE ESTIMATION FOR FIRE DETECTION IN VIDEOS BASED ON OPTICAL FLOW AND NEURAL NETWORKS
2014
International Journal of Research in Engineering and Technology
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Then two feed forward neural networks are used for flame and smoke feature vector classification. The outputs from neural networks are analyzed to find the presence of flame and smoke in the frame. ...
Detecting break out of fire at the initial stage itself is vital for the prevention of material as well as human loss. ...
Fire detection systems based on built-in sensors primarily depend on the reliability and the positional distribution of the sensors. ...
doi:10.15623/ijret.2014.0308050
fatcat:g6ngzc4kd5db5o7rd2usesjtby
An Empirical Study on Some Image Enhancement Techniques towards Identification of Chronical Diseases
2020
International Journal of Computer Applications
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Detection and analysis in medical images, image enhancement techniques are one of the most important phases. ...
A lot of work has been done by different researchers and scientists in the field of image enhancement in the recent past. ...
A comparative analysis with Support Vector Machine (SVM) and Convolution Neural Network (CNN) is carried out in this method. T. M. ...
doi:10.5120/ijca2020919954
fatcat:ogn2qrmqnbg47l555dpje6edpa
Distribution Awareness for AI System Testing
[article]
2021
arXiv
pre-print
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Although recent progress has been made in designing novel testing techniques for DL software, the distribution of generated test data is not taken into consideration. ...
Our results show that this technique is able to filter up to 55.44% of error test case on CIFAR-10 and is 10.05% more effective in enhancing robustness. ...
Srikant, “Enhancing the reliability of
out-of-distribution image detection in neural networks,” in International
Conference on Learning Representations, 2018. [Online]. ...
arXiv:2105.02540v1
fatcat:qbny4vqtm5fpjnj7ce6xtwuwri
Brain Tumor Detection Using Artificial Neural Network Fuzzy Inference System (ANFIS)
2014
International Journal of Computer Applications Technology and Research
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In the proposed methodology, features are extracted from raw images which are then fed to ANFIS (Artificial neural fuzzy inference system).ANFIS being neuro-fuzzy system harness power of both hence it ...
Manual classification of brain tumor is time devastating and bestows ambiguous results. Automatic image classification is emergent thriving research area in medical field. ...
the image for detection of other brain diseases in human being. ...
doi:10.7753/ijcatr0303.1004
fatcat:d36u6jc3yzbupgyjo4zsdgn34q
Brain Tumor Segmentation through Level Based Learning Model
2023
Computer systems science and engineering
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Glioma patients exhibit a different level of challenge in terms of cancer or tumors detection as the Magnetic Resonance Imaging (MRI) images possess varying sizes, shapes, positions, and modalities. ...
Tumors tend to be smaller in size and shape during their premature stages and they can easily evade the algorithms of Convolutional Neural Network (CNN). ...
Parcellation of brains' tissues [32] and cells was introduced along with patch-based neural networks for enhancing the performance of U Net architectures. ...
doi:10.32604/csse.2023.024295
fatcat:kcibq53vajcdbkbvhvjwfmo76e
A Pointer Type Instrument Intelligent Reading System Design Based on Convolutional Neural Networks
2020
Frontiers in Physics
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used to detect and capture the panel area in images; and Convolutional Neural Networks were used to read and predict the characteristic images. ...
Firstly, a histogram normalization transform algorithm was used to optimize the brightness and enhance the contrast of images; then, the feature recognition algorithm You Only Look Once 3rd (YOLOv3) was ...
YL carried out most of the experiments and data analysis. HS contributed to the data analysis and correction. All authors have read and agreed to the published version of the manuscript. ...
doi:10.3389/fphy.2020.618917
fatcat:xixkr5lpxzfl5g5ndtcveqxtzi
Classification of Welding Defects Using Gray Level Histogram Techniques via Neural Network. (Dept. M. ( Production ) )
2020
Bulletin of the Faculty of Engineering. Mansoura University
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weld images and the detection of weld defects. ...
Two main steps to do that, In the first step, image processing techniques, including converting color images to gray scale, filtering image, and resizing were implemented to help in the image array of ...
Recently, there has been an increased interest on the neural researches; in the next some of those researches as an example for detecting welding defect by using neural network. ...
doi:10.21608/bfemu.2020.102839
fatcat:t3cj5beok5ejxcnhfdnyvjd6ye
Salient Object Detection: Integrate Salient Features in the Deep Learning Framework
2019
IEEE Access
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Experiments show that the proposed approach enhances the ability of neural networks to learn specified features and improves the detection effect of salient objects in complex scenes. ...
This paper proposes a novel method to integrate the salient features into the deep learning framework, and design a parallel multi-scale structure of the neural network to enhance the ability to detect ...
End-to-end learning greatly enhances the autonomy of the neural network to acquire feature maps, enabling them to learn effective information in images. ...
doi:10.1109/access.2019.2948062
fatcat:lattgtrb6ng6vhgyl3th6yp7qi
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