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Other applications of machine learning (MLR-APPL)

PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES

Felix Kreuk
Yossi Adi

Phoneme boundary detection plays an essential first step for a variety of speech processing applications such as speaker diarization, speech science, keyword spotting, etc. In this work, we propose a neural architecture coupled with a parameterized structured loss function to learn segmental representations for the task of phoneme boundary detection. First, we evaluated our model when the spoken phonemes were not given as input.

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Authors:
Felix Kreuk
Yossi Adi
Felix Kreuk, Yaniv Sheena, Joseph Keshet, Yossi Adi
Submitted On:
1 June 2020 - 11:44am
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2020-05 - ICASSP - Phoneme Boundary Detection using Learnable Segmental Features.pdf

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[1] Felix Kreuk, Yaniv Sheena, Joseph Keshet, Yossi Adi, "PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5448. Accessed: Jun. 07, 2020.
@article{5448-20,
url = {http://sigport.org/5448},
author = {Felix Kreuk; Yaniv Sheena; Joseph Keshet; Yossi Adi },
publisher = {IEEE SigPort},
title = {PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES},
year = {2020} }
TY - EJOUR
T1 - PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES
AU - Felix Kreuk; Yaniv Sheena; Joseph Keshet; Yossi Adi
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5448
ER -
Felix Kreuk, Yaniv Sheena, Joseph Keshet, Yossi Adi. (2020). PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES. IEEE SigPort. http://sigport.org/5448
Felix Kreuk, Yaniv Sheena, Joseph Keshet, Yossi Adi, 2020. PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES. Available at: http://sigport.org/5448.
Felix Kreuk, Yaniv Sheena, Joseph Keshet, Yossi Adi. (2020). "PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES." Web.
1. Felix Kreuk, Yaniv Sheena, Joseph Keshet, Yossi Adi. PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5448

Semi-Supervised Optimal Transport Methods for Detecting Anomalies

Sylvain Chevallier

Building upon advances on optimal transport and anomaly detection, we propose a generalization of an unsupervised and automatic method for detection of significant deviation from reference signals. Unlike most existing approaches for anomaly detection, our method is built on a non-parametric framework exploiting the optimal transportation to estimate deviation from an observed distribution.

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Authors:
Sylvain Chevallier
Amina Alaoui-Belghiti, Sylvain Chevallier, Eric Monacelli, Guillaume Bao, Eric Azabou
Submitted On:
20 May 2020 - 8:36am
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[1] Amina Alaoui-Belghiti, Sylvain Chevallier, Eric Monacelli, Guillaume Bao, Eric Azabou, "Semi-Supervised Optimal Transport Methods for Detecting Anomalies", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5406. Accessed: Jun. 07, 2020.
@article{5406-20,
url = {http://sigport.org/5406},
author = { Amina Alaoui-Belghiti; Sylvain Chevallier; Eric Monacelli; Guillaume Bao; Eric Azabou },
publisher = {IEEE SigPort},
title = {Semi-Supervised Optimal Transport Methods for Detecting Anomalies},
year = {2020} }
TY - EJOUR
T1 - Semi-Supervised Optimal Transport Methods for Detecting Anomalies
AU - Amina Alaoui-Belghiti; Sylvain Chevallier; Eric Monacelli; Guillaume Bao; Eric Azabou
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5406
ER -
Amina Alaoui-Belghiti, Sylvain Chevallier, Eric Monacelli, Guillaume Bao, Eric Azabou. (2020). Semi-Supervised Optimal Transport Methods for Detecting Anomalies. IEEE SigPort. http://sigport.org/5406
Amina Alaoui-Belghiti, Sylvain Chevallier, Eric Monacelli, Guillaume Bao, Eric Azabou, 2020. Semi-Supervised Optimal Transport Methods for Detecting Anomalies. Available at: http://sigport.org/5406.
Amina Alaoui-Belghiti, Sylvain Chevallier, Eric Monacelli, Guillaume Bao, Eric Azabou. (2020). "Semi-Supervised Optimal Transport Methods for Detecting Anomalies." Web.
1. Amina Alaoui-Belghiti, Sylvain Chevallier, Eric Monacelli, Guillaume Bao, Eric Azabou. Semi-Supervised Optimal Transport Methods for Detecting Anomalies [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5406

UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS

Bowen Liu

We propose a unified compression framework that uses generative adversarial networks (GAN) to compress image and speech signals. The compressed signal is represented by a latent vector fed into a generator network which is trained to produce high-quality signals that minimize a target objective function. To efficiently quantize the compressed signal, non-uniformly quantized optimal latent vectors are identified by iterative back-propagation with ADMM optimization performed for each iteration.

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Bowen Liu
Submitted On:
16 May 2020 - 4:30pm
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[1] , "UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5379. Accessed: Jun. 07, 2020.
@article{5379-20,
url = {http://sigport.org/5379},
author = { },
publisher = {IEEE SigPort},
title = {UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS},
year = {2020} }
TY - EJOUR
T1 - UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5379
ER -
. (2020). UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS. IEEE SigPort. http://sigport.org/5379
, 2020. UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS. Available at: http://sigport.org/5379.
. (2020). "UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS." Web.
1. . UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5379

LOW MUTUAL AND AVERAGE COHERENCE DICTIONARY LEARNING USING CONVEX APPROXIMATION

Javad Parsa

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Authors:
Javad Parsa
Submitted On:
15 May 2020 - 6:11pm
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1866.pdf

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[1] , "LOW MUTUAL AND AVERAGE COHERENCE DICTIONARY LEARNING USING CONVEX APPROXIMATION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5357. Accessed: Jun. 07, 2020.
@article{5357-20,
url = {http://sigport.org/5357},
author = { },
publisher = {IEEE SigPort},
title = {LOW MUTUAL AND AVERAGE COHERENCE DICTIONARY LEARNING USING CONVEX APPROXIMATION},
year = {2020} }
TY - EJOUR
T1 - LOW MUTUAL AND AVERAGE COHERENCE DICTIONARY LEARNING USING CONVEX APPROXIMATION
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5357
ER -
. (2020). LOW MUTUAL AND AVERAGE COHERENCE DICTIONARY LEARNING USING CONVEX APPROXIMATION. IEEE SigPort. http://sigport.org/5357
, 2020. LOW MUTUAL AND AVERAGE COHERENCE DICTIONARY LEARNING USING CONVEX APPROXIMATION. Available at: http://sigport.org/5357.
. (2020). "LOW MUTUAL AND AVERAGE COHERENCE DICTIONARY LEARNING USING CONVEX APPROXIMATION." Web.
1. . LOW MUTUAL AND AVERAGE COHERENCE DICTIONARY LEARNING USING CONVEX APPROXIMATION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5357

Multi-step Online Unsupervised Domain Adaptation

Jihoon Moon

In this paper, we address the Online Unsupervised Domain Adaptation (OUDA) problem, where the target data are unlabelled and arriving sequentially. The traditional methods on the OUDA problem mainly focus on transforming each arriving target data to the source domain, and they do not sufficiently consider the temporal coherency and accumulative statistics among the arriving target data. We propose a multi-step framework for the OUDA problem, which institutes a novel method to compute the mean-target subspace inspired by the geometrical interpretation on the Euclidean space.

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Authors:
Jihoon Moon
J. H. Moon, Debasmit Das, and C. S. George Lee
Submitted On:
14 May 2020 - 11:58pm
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ICASSP2020_Jihoon Moon_final_4.pdf

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[1] J. H. Moon, Debasmit Das, and C. S. George Lee, "Multi-step Online Unsupervised Domain Adaptation", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5331. Accessed: Jun. 07, 2020.
@article{5331-20,
url = {http://sigport.org/5331},
author = {J. H. Moon; Debasmit Das; and C. S. George Lee },
publisher = {IEEE SigPort},
title = {Multi-step Online Unsupervised Domain Adaptation},
year = {2020} }
TY - EJOUR
T1 - Multi-step Online Unsupervised Domain Adaptation
AU - J. H. Moon; Debasmit Das; and C. S. George Lee
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5331
ER -
J. H. Moon, Debasmit Das, and C. S. George Lee. (2020). Multi-step Online Unsupervised Domain Adaptation. IEEE SigPort. http://sigport.org/5331
J. H. Moon, Debasmit Das, and C. S. George Lee, 2020. Multi-step Online Unsupervised Domain Adaptation. Available at: http://sigport.org/5331.
J. H. Moon, Debasmit Das, and C. S. George Lee. (2020). "Multi-step Online Unsupervised Domain Adaptation." Web.
1. J. H. Moon, Debasmit Das, and C. S. George Lee. Multi-step Online Unsupervised Domain Adaptation [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5331

INSTANT ADAPTIVE LEARNING: AN ADAPTIVE FILTER BASED FAST LEARNING MODEL CONSTRUCTION FOR SENSOR SIGNAL TIME SERIES CLASSIFICATION ON EDGE DEVICES

Arijit Ukil

Construction of learning model under computational and energy constraints, particularly in highly limited training time requirement is a critical as well as unique necessity of many practical IoT applications that use time series sensor signal analytics for edge devices. Yet, majority of the state-of-the-art algorithms and solutions attempt to achieve high performance objective (like test accuracy) irrespective of the computational constraints of real-life applications.

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Authors:
Arijit Ukil
Arpan Pal, Arijit Ukil, Trisrota Deb, Ishan Sahu, Angshul Majumdar
Submitted On:
14 May 2020 - 12:56pm
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ICASSP_2020_presentation_Arijit.pdf

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[1] Arpan Pal, Arijit Ukil, Trisrota Deb, Ishan Sahu, Angshul Majumdar, "INSTANT ADAPTIVE LEARNING: AN ADAPTIVE FILTER BASED FAST LEARNING MODEL CONSTRUCTION FOR SENSOR SIGNAL TIME SERIES CLASSIFICATION ON EDGE DEVICES", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5318. Accessed: Jun. 07, 2020.
@article{5318-20,
url = {http://sigport.org/5318},
author = {Arpan Pal; Arijit Ukil; Trisrota Deb; Ishan Sahu; Angshul Majumdar },
publisher = {IEEE SigPort},
title = {INSTANT ADAPTIVE LEARNING: AN ADAPTIVE FILTER BASED FAST LEARNING MODEL CONSTRUCTION FOR SENSOR SIGNAL TIME SERIES CLASSIFICATION ON EDGE DEVICES},
year = {2020} }
TY - EJOUR
T1 - INSTANT ADAPTIVE LEARNING: AN ADAPTIVE FILTER BASED FAST LEARNING MODEL CONSTRUCTION FOR SENSOR SIGNAL TIME SERIES CLASSIFICATION ON EDGE DEVICES
AU - Arpan Pal; Arijit Ukil; Trisrota Deb; Ishan Sahu; Angshul Majumdar
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5318
ER -
Arpan Pal, Arijit Ukil, Trisrota Deb, Ishan Sahu, Angshul Majumdar. (2020). INSTANT ADAPTIVE LEARNING: AN ADAPTIVE FILTER BASED FAST LEARNING MODEL CONSTRUCTION FOR SENSOR SIGNAL TIME SERIES CLASSIFICATION ON EDGE DEVICES. IEEE SigPort. http://sigport.org/5318
Arpan Pal, Arijit Ukil, Trisrota Deb, Ishan Sahu, Angshul Majumdar, 2020. INSTANT ADAPTIVE LEARNING: AN ADAPTIVE FILTER BASED FAST LEARNING MODEL CONSTRUCTION FOR SENSOR SIGNAL TIME SERIES CLASSIFICATION ON EDGE DEVICES. Available at: http://sigport.org/5318.
Arpan Pal, Arijit Ukil, Trisrota Deb, Ishan Sahu, Angshul Majumdar. (2020). "INSTANT ADAPTIVE LEARNING: AN ADAPTIVE FILTER BASED FAST LEARNING MODEL CONSTRUCTION FOR SENSOR SIGNAL TIME SERIES CLASSIFICATION ON EDGE DEVICES." Web.
1. Arpan Pal, Arijit Ukil, Trisrota Deb, Ishan Sahu, Angshul Majumdar. INSTANT ADAPTIVE LEARNING: AN ADAPTIVE FILTER BASED FAST LEARNING MODEL CONSTRUCTION FOR SENSOR SIGNAL TIME SERIES CLASSIFICATION ON EDGE DEVICES [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5318

Detect insider attacks using CNN in Decentralized Optimization

Gangqiang LI

This paper studies the security issue of a gossip-based distributed projected gradient (DPG) algorithm, when it is applied for solving a decentralized multi-agent optimization. It is known that the gossip-based DPG algorithm is vulnerable to insider attacks because each agent locally estimates its (sub)gradient without any supervision. This work leverages the convolutional neural network (CNN) to perform the detection and localization of the insider attackers.

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Authors:
Gangqiang LI
Sissi Xiaoxiao Wu, Shengli Zhang, Qiang Li
Submitted On:
14 May 2020 - 8:45am
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Detect insider attacks using CNN in Decentralized Optimization

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[1] Sissi Xiaoxiao Wu, Shengli Zhang, Qiang Li, "Detect insider attacks using CNN in Decentralized Optimization", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5296. Accessed: Jun. 07, 2020.
@article{5296-20,
url = {http://sigport.org/5296},
author = {Sissi Xiaoxiao Wu; Shengli Zhang; Qiang Li },
publisher = {IEEE SigPort},
title = {Detect insider attacks using CNN in Decentralized Optimization},
year = {2020} }
TY - EJOUR
T1 - Detect insider attacks using CNN in Decentralized Optimization
AU - Sissi Xiaoxiao Wu; Shengli Zhang; Qiang Li
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5296
ER -
Sissi Xiaoxiao Wu, Shengli Zhang, Qiang Li. (2020). Detect insider attacks using CNN in Decentralized Optimization. IEEE SigPort. http://sigport.org/5296
Sissi Xiaoxiao Wu, Shengli Zhang, Qiang Li, 2020. Detect insider attacks using CNN in Decentralized Optimization. Available at: http://sigport.org/5296.
Sissi Xiaoxiao Wu, Shengli Zhang, Qiang Li. (2020). "Detect insider attacks using CNN in Decentralized Optimization." Web.
1. Sissi Xiaoxiao Wu, Shengli Zhang, Qiang Li. Detect insider attacks using CNN in Decentralized Optimization [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5296

Reconstruction of FRI Signals Using Deep Neural Network Approaches

Vincent C. H. Leung
Pier Luigi Dragotti

Finite Rate of Innovation (FRI) theory considers sampling and reconstruction of classes of non-bandlimited continuous signals that have a small number of free parameters, such as a stream of Diracs. The task of reconstructing FRI signals from discrete samples is often transformed into a spectral estimation problem and solved using Prony's method and matrix pencil method which involve estimating signal subspaces. They achieve an optimal performance given by the Cramér-Rao bound yet break down at a certain peak signal-to-noise ratio (PSNR).

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Authors:
Vincent C. H. Leung
Pier Luigi Dragotti
Vincent C. H. Leung, Jun-Jie Huang, Pier Luigi Dragotti
Submitted On:
14 May 2020 - 7:28am
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ICASSP2020_Slides_final.pdf

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[1] Vincent C. H. Leung, Jun-Jie Huang, Pier Luigi Dragotti, "Reconstruction of FRI Signals Using Deep Neural Network Approaches", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5285. Accessed: Jun. 07, 2020.
@article{5285-20,
url = {http://sigport.org/5285},
author = {Vincent C. H. Leung; Jun-Jie Huang; Pier Luigi Dragotti },
publisher = {IEEE SigPort},
title = {Reconstruction of FRI Signals Using Deep Neural Network Approaches},
year = {2020} }
TY - EJOUR
T1 - Reconstruction of FRI Signals Using Deep Neural Network Approaches
AU - Vincent C. H. Leung; Jun-Jie Huang; Pier Luigi Dragotti
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5285
ER -
Vincent C. H. Leung, Jun-Jie Huang, Pier Luigi Dragotti. (2020). Reconstruction of FRI Signals Using Deep Neural Network Approaches. IEEE SigPort. http://sigport.org/5285
Vincent C. H. Leung, Jun-Jie Huang, Pier Luigi Dragotti, 2020. Reconstruction of FRI Signals Using Deep Neural Network Approaches. Available at: http://sigport.org/5285.
Vincent C. H. Leung, Jun-Jie Huang, Pier Luigi Dragotti. (2020). "Reconstruction of FRI Signals Using Deep Neural Network Approaches." Web.
1. Vincent C. H. Leung, Jun-Jie Huang, Pier Luigi Dragotti. Reconstruction of FRI Signals Using Deep Neural Network Approaches [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5285

Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data

Takayuki Katsuki

This paper addresses the learning task of estimating driver drowsiness from the signals of car acceleration sensors. Since even drivers themselves cannot perceive their own drowsiness in a timely manner unless they use burdensome invasive sensors, obtaining labeled training data for each timestamp is not a realistic goal. To deal with this difficulty, we formulate the task as a weakly supervised learning. We only need to add labels for each complete trip, not for every timestamp independently.

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Authors:
Takayuki Katsuki
Kun Zhao,Takayuki Yoshizumi
Submitted On:
14 May 2020 - 2:52am
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Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data

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[1] Kun Zhao,Takayuki Yoshizumi, "Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5240. Accessed: Jun. 07, 2020.
@article{5240-20,
url = {http://sigport.org/5240},
author = {Kun Zhao;Takayuki Yoshizumi },
publisher = {IEEE SigPort},
title = {Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data},
year = {2020} }
TY - EJOUR
T1 - Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data
AU - Kun Zhao;Takayuki Yoshizumi
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5240
ER -
Kun Zhao,Takayuki Yoshizumi. (2020). Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data. IEEE SigPort. http://sigport.org/5240
Kun Zhao,Takayuki Yoshizumi, 2020. Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data. Available at: http://sigport.org/5240.
Kun Zhao,Takayuki Yoshizumi. (2020). "Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data." Web.
1. Kun Zhao,Takayuki Yoshizumi. Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5240

CONFIRMNET: CONVOLUTIONAL FIRMNET AND APPLICATION TO IMAGE DENOISING AND INPAINTING

PRAVEEN POKALA

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Authors:
PRAVEEN POKALA
Praveen Kumar Pokala, Prakash Kumar Uttam, and Chandra Sekhar Seelamantula
Submitted On:
14 May 2020 - 1:41am
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ICASSP_2020_ConFirmNet.pdf

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[1] Praveen Kumar Pokala, Prakash Kumar Uttam, and Chandra Sekhar Seelamantula, "CONFIRMNET: CONVOLUTIONAL FIRMNET AND APPLICATION TO IMAGE DENOISING AND INPAINTING", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5224. Accessed: Jun. 07, 2020.
@article{5224-20,
url = {http://sigport.org/5224},
author = {Praveen Kumar Pokala; Prakash Kumar Uttam; and Chandra Sekhar Seelamantula },
publisher = {IEEE SigPort},
title = {CONFIRMNET: CONVOLUTIONAL FIRMNET AND APPLICATION TO IMAGE DENOISING AND INPAINTING},
year = {2020} }
TY - EJOUR
T1 - CONFIRMNET: CONVOLUTIONAL FIRMNET AND APPLICATION TO IMAGE DENOISING AND INPAINTING
AU - Praveen Kumar Pokala; Prakash Kumar Uttam; and Chandra Sekhar Seelamantula
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5224
ER -
Praveen Kumar Pokala, Prakash Kumar Uttam, and Chandra Sekhar Seelamantula. (2020). CONFIRMNET: CONVOLUTIONAL FIRMNET AND APPLICATION TO IMAGE DENOISING AND INPAINTING. IEEE SigPort. http://sigport.org/5224
Praveen Kumar Pokala, Prakash Kumar Uttam, and Chandra Sekhar Seelamantula, 2020. CONFIRMNET: CONVOLUTIONAL FIRMNET AND APPLICATION TO IMAGE DENOISING AND INPAINTING. Available at: http://sigport.org/5224.
Praveen Kumar Pokala, Prakash Kumar Uttam, and Chandra Sekhar Seelamantula. (2020). "CONFIRMNET: CONVOLUTIONAL FIRMNET AND APPLICATION TO IMAGE DENOISING AND INPAINTING." Web.
1. Praveen Kumar Pokala, Prakash Kumar Uttam, and Chandra Sekhar Seelamantula. CONFIRMNET: CONVOLUTIONAL FIRMNET AND APPLICATION TO IMAGE DENOISING AND INPAINTING [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5224

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