{"id":19361,"date":"2022-10-12T13:46:25","date_gmt":"2022-10-12T17:46:25","guid":{"rendered":"https:\/\/www.crim.ca\/scientific-publications\/"},"modified":"2026-01-12T16:35:22","modified_gmt":"2026-01-12T21:35:22","slug":"publications-scientifiques-archive","status":"publish","type":"page","link":"https:\/\/www.crim.ca\/en\/publications-scientifiques-archive\/","title":{"rendered":"Scientific Publications"},"content":{"rendered":"\t\t
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\n All<\/a>Acte de conf\u00e9rence<\/a>Livre<\/a>P\u00e9riodique<\/a>Rapport technique<\/a>Th\u00e8se \/ M\u00e9moire<\/a><\/div><\/div>

A Versatile and Efficient Approach to Summarize Speech Into Utterance-Level Representations<\/a><\/h3>

Monteiro, J., Alam, J., Falk, T., “A Versatile and Efficient Approach to Summarize Speech Into Utterance-Level Representations”, NeurIPS 2021, Efficient Natural Language and Speech Processing (ENLSP) Workshop, December 13, 2021.<\/p>

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ABC System Description for NIST SRE 2021<\/a><\/h3>

Alam, J. et al, “ABC System Description for NIST SRE 2021”, NIST SRE 2021 Workshop, December 14-16, 2021.<\/p>

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Investigation on Instance Mixup Regularization Strategies for Self-supervised Speaker Representation Learning<\/a><\/h3>

Kang, W., Alam, J., Fathan, A., “Investigation on Instance Mixup Regularization Strategies for Self-supervised Speaker Representation Learning”, AAAI 2022 Self-supervision in Audio and Speech (AAAI 2022 SAS), 28 f\u00e9vrier 2022.<\/p>

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Joint Service Function Chain Embedding and Routing in Cloud-based NFV: A Deep Q-Learning Based Approach<\/a><\/h3>

Tran, T. D., Jaumard, B., Duong, H., Nguyen, K.-K., “Joint Service Function Chain Embedding and Routing in Cloud-based NFV: A Deep Q-Learning Based Approach,” 2021 IEEE 4th 5G World Forum (5GWF), November 19, 2021, pp. 171-175.<\/p>

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Hybrid Network with Multi-Level Global-Local Statistics Pooling for Robust Text-Independent Speaker Recognition<\/a><\/h3>

Kang, W., Alam, J., Fathan, A., “Hybrid Network with Multi-Level Global-Local Statistics Pooling for Robust Text-Independent Speaker Recognition”, 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 3 f\u00e9vrier 2022.<\/p>

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Identification of Compromised IoT Devices: Combined Approach Based on Energy Consumption and Network Traffic Analysis<\/a><\/h3>

Jaafar, F., Ameyed, D., Barrak, A., Cheriet, M., “Identification of Compromised IoT Devices: Combined Approach Based on Energy Consumption and Network Traffic Analysis”, 2021 IEEE 21st International Conference on Software Quality, Reliability, and Security (QRS), December 6-10, 2021, pp. 514-523.<\/p>

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UD on Software Requirements: Application and Challenges<\/a><\/h3>

Hassert, N., M\u00e9nard, P. A., Galy, E., “UD on Software Requirements: Application and Challenges”, Fifth Workshop on Universal Dependencies, Syntaxfest 2021, March 21-25, 2022.<\/p>

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Adaptation of Deep Siamese Neural Networks for Video Face Recognition<\/a><\/h3>

Lemoine St-Andr\u00e9, Hugo, “Adaptation of Deep Siamese Neural Networks for Video Face Recognition”, Master’s thesis, \u00c9cole de Technologie Sup\u00e9rieure, Montr\u00e9al, January 24, 2022. Direction: \u00c9. Granger. Co-supervision: M. Dahmane.<\/p>

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Efficient Make-Before-Break Layer 2 Reoptimization<\/a><\/h3>

Duong, H., Jaumard, B., Coudert, D., Armolavicius, R., “Efficient Make-Before-Break Layer 2 Reoptimization”, in IEEE\/ACM Transactions on Networking, Vol. 29, No. 1, in IEEE\/ACM Transactions on Networking, Vol. 29, No. 1, in IEEE\/ACM Transactions on Networking, Vol. 29, No. 1, in IEEE\/ACM Transactions on Networking. 5, pp. 1910-1921, Oct. 2021.<\/p>

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Text-Independent Speaker Verification Employing CNN-LSTM-TDNN Hybrid Networks<\/a><\/h3>

Alam, J., Fathan, A., Kang, W., “Text-Independent Speaker Verification Employing CNN-LSTM-TDNN Hybrid Networks”, 23rd International Conference on Speech and Computer (SPECOM 2021), St. Petersburg, Russia, September 27-30, 2021.<\/p>

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End-to-End Voice Spoofing Detection Employing Time Delay Neural Networks and Higher Order Statistics<\/a><\/h3>

Alam, J., Fathan, A., Kang, W., “End-to-End Voice Spoofing Detection Employing Time Delay Neural Networks and Higher Order Statistics,” 23rd International Conference on Speech and Computer (SPECOM 2021), St. Petersburg, Russia, September 27-30, 2021.<\/p>

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An Ensemble Approach for the Diagnosis of COVID-19 from Speech and Cough Sounds<\/a><\/h3>

Fathan, A., Alam, J., Kang, W., “An Ensemble Approach for the Diagnosis of COVID-19 from Speech and Cough Sounds”, 23rd International Conference on Speech and Computer (SPECOM 2021), St. Petersburg, Russia, September 27-30, 2021.<\/p>

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Investigation on Activation Functions for Robust End-to-End Spoofing Attack Detection System<\/a><\/h3>

Kang, W., Alam, J., Fathan, A., “Investigation on Activation Functions for Robust End-to-End Spoofing Attack Detection System”, Automatic Speaker Verification and Spoofing Countermeasures Challenge (ASVspoof 2021 Workshop – a satellite workshop of INTERSPEECH 2021), September 16, 2021.<\/p>

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CRIM’s System Description for the ASVSpoof2021 Challenge<\/a><\/h3>

Kang, W., Alam, J., Fathan, A., “CRIM’s System Description for the ASVSpoof2021 Challenge”, Automatic Speaker Verification and Spoofing Countermeasures Challenge (ASVspoof 2021 Workshop – a satellite workshop of INTERSPEECH 2021), September 16, 2021.<\/p>

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Automatic classification of point clouds from airborne LiDAR using a continuous convolution network<\/a><\/h3>

M. Turgeon-Pelchat, “Automatic classification of point clouds from airborne LiDAR using a continuous convolution network”. Master’s thesis, Universit\u00e9 de Sherbrooke, September 14, 2021. Management: Y. Bouroubi. Co-directors: S. Foucher and N. Sabo.<\/p>

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Team02 Text-Independent Speaker Verification System for SdSV Challenge 2021<\/a><\/h3>

Kang, W., Kim, N.S., “Team02 Text-Independent Speaker Verification System for SdSV Challenge 2021”, INTERSPEECH 2021, Brno, Czech Republic, August 30 – September 3, 2021, pp. 2312-2316.<\/p>

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Multispecies Detection and Identification of African Mammals in Aerial Imagery Using Convolutional Neural Networks<\/a><\/h3>

Delplanque, A., Foucher, S., Lejeune, P., Linchant, J., Th\u00e9au, J., “Multispecies Detection and Identification of African Mammals in Aerial Imagery Using Convolutional Neural Networks”, in Remote Sensing in Ecology and Conservation, 14 p., August 10, 2021.<\/p>

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Minimum Disturbance Routing to Optimize Bandwidth Usage<\/a><\/h3>

Duong, H., Jaumard, B., Coudert, D., “Minimum Disturbance Routing to Optimize Bandwidth Usage”, 2021 International Conference on Optical Network Design and Modelling (ONDM), Gothenburg, Sweden, June 28 to July 1, 2021, pp. 1-6.<\/p>

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Algorithmic developments to improve the results of RADAR interferometry in urban environments<\/a><\/h3>

A. Tlili, “Algorithmic developments for improving RADAR interferometry results in urban environments”. Ph.D. thesis, Department of Geography, Universit\u00e9 de Montr\u00e9al, July 14, 2021.<\/p>

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Sidewalk characterization: automatic extraction of geographical features from urban panoramic images using convolutional neural networks<\/a><\/h3>

R. Tavon, “Sidewalk characterization: automatic extraction of geographic features from urban panoramic images using convolutional neural networks”. Master’s thesis, Universit\u00e9 de Sherbrooke, June 8, 2021. Management: Y. Bouroubi, L. Bellalite and S. Foucher.<\/p>

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Deep Learning-Based Classification of Large-Scale Airborne LiDAR Point Cloud<\/a><\/h3>

Turgeon-Pelchat, M., Foucher, S., Bouroubi, Y., “Deep Learning-Based Classification of Large-Scale Airborne LiDAR Point Cloud”, in Canadian Journal of Remote Sensing, Vol. 47, No. 3, pp. 381-395, May 27, 2021.<\/p>

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Extended Forgery Detection Framework for COVID-19 Medical Data Using Convolutional Neural Network<\/a><\/h3>

Gill, S. H., Sheikh, N. A., Rajpar, S., Abidin, Z., Jhanjhi, N. Z., Ahmad, M., Razzaq, M. A., Alshamrani, S. S., Malik, Y., Jaafar, F., “Extended Forgery Detection Framework for COVID-19 Medical Data Using Convolutional Neural Network”, in Computers, Materials & Continua, Vol. 68, No.3, pp. 3773-3787, May 6, 2021.<\/p>

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On the Impact of Interlanguage Dependencies in Multilanguage Systems Empirical Case Study on Java Native Interface Applications (JNI)<\/a><\/h3>

M. Grichi, M. Abidi, F. Jaafar, E. E. Eghan and B. Adams, “On the Impact of Interlanguage Dependencies in Multilanguage Systems Empirical Case Study on Java Native Interface Applications (JNI),” in IEEE Transactions on Reliability, Volume 70, No. 1, pp. 428-440, March 2021.<\/p>

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OGC Testbed-16: Machine Learning Engineering Report<\/a><\/h3>

P. Vretanos, and al, “OGC Testbed-16: Machine Learning Engineering Report”, OGC Public Engineering Report, OGC 20-015r2, February 15, 2021.<\/p>

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Social media: perspectives on the challenges of cybersecurity, algorithmic governmentality and artificial intelligence<\/a><\/h3>

S. Pierre and F. Jaafar. Social media: perspectives on the challenges of cybersecurity, algorithmic governmentality and artificial intelligence. Presses de l’Universit\u00e9 Laval. February 11, 2021, 192 pages.<\/p>

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CRIM\u2019s System Description for the Third Edition of DIHARD Challenge 2020<\/a><\/h3>

J. Alam, and V. Gupta, \u00ab CRIM\u2019s System Description for the Third Edition of DIHARD Challenge 2020 \u00bb, in The Third DIHARD Speech Diarization Challenge Workshop, January 23, 2021.<\/p>

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Investigating Design Anti-pattern and Design Pattern Mutations and Their Change- and Fault-proneness<\/a><\/h3>

Z.A. Kermansaravi, M.S. Rahman, F. Khomh, F. Jaafar and Y.-G. Gu\u00e9h\u00e9neuc, “Investigating Design Anti-pattern and Design Pattern Mutations and Their Change- and Fault-proneness” in Empirical Software Engineering, Springer, Vol. 26, No. 1, Article 9, January 15, 2021.<\/p>

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Train fast while reducing false positives: improving animal classification performance using Convolutional Neural Networks<\/a><\/h3>

M. Moreni, J. Theau and S. Foucher, “Train fast while reducing false positives: improving animal classification performance using Convolutional Neural Networks”, in Geomatics, Vol. 1, pp. 34-49, January 15, 2021.<\/p>

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OGC Testbed-16: Data Access and Processing Engineering Report<\/a><\/h3>

P. Vretanos, and al, “OGC Testbed-16: Data Access and Processing Engineering Report”, OGC Public Engineering Report, OGC 20-016, January 13, 2021.<\/p>

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Training Data Quality<\/a><\/h3>

H. P\u00f6l\u00f6nen and al, “Training Data Quality”, IVVES Project, Deliverable 2.2, 52 pages, December 31, 2020.<\/p>

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Validation methods and techniques for evolving systems considering use case requirements<\/a><\/h3>

O. Nguena Timo, A. Petrenko and al., \u00ab Validation methods and techniques for evolving systems considering use case requirements \u00bb, IVVES Project, Deliverable 3.2, 55 pages, December 21, 2020.<\/p>

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Identifying grapevine phytosanitary problems within the plot: combining ultra-high spatial resolution imagery and deep learning<\/a><\/h3>

J. Boulent, “Identification of grapevine phytosanitary problems within the plot: combining ultra-high spatial resolution imagery and deep learning”. PhD thesis, Universit\u00e9 de Sherbrooke, December 18, 2020.<\/p>

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New Interferometric Phase Unwrapping Method Based on Energy Minimization From Contextual Modeling<\/a><\/h3>

A. Tlili, F. Cavayas, S. Foucher and G. L. Siles, “New Interferometric Phase Unwrapping Method Based on Energy Minimization From Contextual Modeling”, in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Volume 13, pp. 6524-6532, December 2020.<\/p>

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The Indigenous Languages Technology Project at NRC Canada: an empowerment-oriented approach to developing language software<\/a><\/h3>

R. Kuhn et al, “The Indigenous Languages Technology Project at NRC Canada: an empowerment-oriented approach to developing language software”, in Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), December 8-13, 2020.<\/p>

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Automatic Detection of Flavescence Dor\u00e9e Symptoms Across White Grapevine Varieties Using Deep Learning<\/a><\/h3>

J. Boulent, P.L. St-Charles, S. Foucher and J. Th\u00e9au, “Automatic Detection of Flavescence Dor\u00e9e Symptoms Across White Grapevine Varieties Using Deep Learning”, in Frontiers in Artificial Intelligence, Vol. 3, 96 p., November 30, 2020.<\/p>

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An Ensemble Approach to Unsupervised Anomalous Sound Detection<\/a><\/h3>

J. Alam, G. Boulianne, V. Gupta and F. Abderrahim, “An Ensemble Approach to Unsupervised Anomalous Sound Detection” in DCASE 2020 Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring Challenge, November 2020.<\/p>

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A Multi-condition Training Strategy for Countermeasures Against Spoofing Attacks to Speaker Recognizers<\/a><\/h3>

J. Montiero, J. Alam, and T. Falk, “A Multi-condition Training Strategy for Countermeasures Against Spoofing Attacks to Speaker Recognizers”, in Speaker and Language Recognition Workshop (Odyssey 2020), Tokyo, Japan, November, 2020.<\/p>

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Analysis of ABC Submission to NIST SRE 2019 CMN and VAST Challenge<\/a><\/h3>

J. Alam, G. Boulianne, and al, “Analysis of ABC Submission to NIST SRE 2019 CMN and VAST Challenge”, in Speaker and Language Recognition Workshop (Odyssey 2020), Tokyo, Japan, November, 2020, pp. 289-295.<\/p>

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A Circular Invariant Convolution Model-Based Mapping for Multimodal Change Detection<\/a><\/h3>

R. Touati, M. Mignotte and M. Dahmane, “A Circular Invariant Convolution Model-Based Mapping for Multimodal Change Detection”, in Advances in Science, Technology and Engineering Systems Journal, Vol. 5, No. 5, pp. 1288-1298, October 26, 2020.<\/p>

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OGC Earth Observation Applications Pilot: CRIM Engineering Report<\/a><\/h3>

T. Landry, F. Charette-Migneault, M. Beaulieu, M. Provencher, L.-D. Perron, D. Byrns and S. Foucher, “OGC Earth Observation Applications Pilot: CRIM Engineering Report”, OGC Public Engineering Report, OGC 20-045, October 26, 2020.<\/p>

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An Approach to Evaluating Learning Algorithms for Decision Trees<\/a><\/h3>

T. Xiao, O. Nguena-Timo, F. Avellaneda, Y. Malik, S. D. Bruda, “An Approach to Evaluating Learning Algorithms for Decision Trees”, arXiv:2010.13665, 19 p., 2020.<\/p>

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SdSV Challenge 2020: Large-Scale Evaluation of Short\u2010Duration Speaker Verification<\/a><\/h3>

H. Zeinali, K. A. Lee, J. Alam, and L. Burget, \u00ab SdSV Challenge 2020: Large-Scale Evaluation of Short\u2010Duration Speaker Verification \u00bb, in INTERSPEECH 2020, Shanghai, Chine, October 25-29, 2020.<\/p>

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A Study of Inductive Biases for Unsupervised Speech Representation Learning<\/a><\/h3>

G. Boulianne, “A Study of Inductive Biases for Unsupervised Speech Representation Learning”, in IEEE\/ACM Transactions on Acoustics, Speech and Language Processing, Vol. 28, pp. 2781-2795, October 13, 2020.<\/p>

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CRIM’s Automatic Speech Recognition and Speech Activity Detection Systems Description for the 2020 edition of NIST Open Speech Analytic Technologies Evaluation<\/a><\/h3>

V. Gupta, J. Alam, G. Boulianne, “CRIM’s Automatic Speech Recognition and Speech Activity Detection Systems Description for the 2020 edition of NIST Open Speech Analytic Technologies Evaluation” in OpenSAT 2020 Evaluation Workshop, USA, September 16, 2020.<\/p>

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On the Use of Blind Channel Response Estimation and a Residual Neural Network to Detect Physical Access Attacks to Speaker Verification Systems<\/a><\/h3>

A. R. Avila, J. Alam, F. O. Costa Prado, D. O’Shaughnessy, and T. H. Falk, “On the Use of Blind Channel Response Estimation and a Residual Neural Network to Detect Physical Access Attacks to Speaker Verification Systems” in Computer Speech & Language Journal, Vol. 66, March 2021 (first online October 20, 2020).<\/p>

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Generalized End-to-End Detection of Spoofing Attacks to Automatic Speaker Recognizers<\/a><\/h3>

J. Montiero, J, Alam, and T. Falk, “Generalized End-to-End Detection of Spoofing Attacks to Automatic Speaker Recognizers”, Special issue of Computer Speech & Language Journal, Vol. 63, September, 2020, Article 101096.<\/p>

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Identifying African Mammal Species in Aerial Images With Object Detection Algorithms<\/a><\/h3>

A. Delplanque, “Identifying African Mammal Species in Aerial Images With Object Detection Algorithms”. Master’s thesis, University of Li\u00e8ge, Belgium, September 2020.<\/p>

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Unsupervised Speech Representation Learning<\/a><\/h3>

G. Boulianne, “Unsupervised Speech Representation Learning”. PhD thesis, \u00c9cole de Technologie Sup\u00e9rieure, 2020.<\/p>

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Demystifying the Cyber Attribution: An Exploratory Study<\/a><\/h3>

F. Jaafar, F. Avellaneda, and E.-H. Alikacem, “Demystifying the Cyber Attribution: An Exploratory Study” in IEEE Cyber Science and Technology Congress (CyberSciTech 2020), August 18-24, 2020.<\/p>

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Behavioral Study of Malware Affecting Financial Institutions and Clients<\/a><\/h3>

R. Mishra, S. Butakov, F. Jaafar, and N. Memon, “Behavioral Study of Malware Affecting Financial Institutions and Clients” in IEEE Cyber Science and Technology Congress (CyberSciTech 2020), August 18-24, 2020.<\/p>

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A short description of the solver EvalMaxSAT<\/a><\/h3>

F. Avellaneda, “A short description of the solver EvalMaxSAT”, in MaxSAT Evaluation 2020 (MSE 2020), pp. 8-9, Alghero, Italy, July 5-9 2020.<\/p>

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An End-to-End Approach for the Verification Problem: Learning The Right Distance<\/a><\/h3>

J. Monteiro, I. Albuquerque, J. Alam, D. Hjelm, T. Falk, and C. Jacques, “An End-to-End Approach for the Verification Problem: Learning The Right Distance”, in37th International Conference on Machine Learning (ICML 2020), July, 12-18, 2020.<\/p>

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A Preliminary Systematic Mapping on Software Engineering for Robotic Systems: A Software Quality Perspective<\/a><\/h3>

M. Santos, F. Jafaar, B. Minetto Napole\u00e3o, F. Petrillo, and D. Ameyed, “A Preliminary Systematic Mapping on Software Engineering for Robotic Systems: A Software Quality Perspective”, IEEE\/ACM 42nd International Conference on Software Engineering Workshops (ICSEW’20), June 2020, pp. 647-654.<\/p>

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Study of Security Issues in Platform-as-a-Service (PaaS) Cloud Model<\/a><\/h3>

F. Jaafar, W. Isharufe, and S. Butakov, “Study of Security Issues in Platform-as-a-Service (PaaS) Cloud Model”, in IEEE 2nd International Conference on Electrical, Communication and Computer Engineering (ICECCE 2020), Istanbul, Turkey, June 12-13, 2020.<\/p>

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Securing the Authentication Process of LTE Base Stations<\/a><\/h3>

A. B. Seyi, F. Jafaar, and R. Ruhl, “Securing the Authentication Process of LTE Base Stations”, in IEEE 2nd International Conference on Electrical, Communication and Computer Engineering (ICECCE 2020), Istanbul, Turkey, June 12-13, 2020.<\/p>

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The Indigenous Languages Technology Project at NRC Canada: an empowerment-oriented approach to developing language software<\/a><\/h3>

R. Kuhn and al., “The Indigenous Languages Technology Project at NRC Canada: an empowerment-oriented approach to developing language software”, NRC Publication, June, 2020.<\/p>

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On the Use of the I-vector Speech Representation for Instrumental Quality Measurement<\/a><\/h3>

A. R. Avila, J. Alam, D. O’Shaughnessy, and T. Falk, “On the Use of the I-vector Speech Representation for Instrumental Quality Measurement” in Quality and User Experience Journal, Springer, Volume 5, Number 6, June, 2020, pp. 1-14.<\/p>

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An Ensemble Based Approach for Generalized Detection of Spoofing Attacks to Automatic Speaker Recognizers<\/a><\/h3>

J. Monteiro, J. Alam, and T. Falk, “An Ensemble Based Approach for Generalized Detection of Spoofing Attacks to Automatic Speaker Recognizers,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2020). pp. 6599-6603, Barcelona, Spain, May 4-8, 2020.<\/p>

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Automatic Transcription Challenges for Inuktitut, a Low-Resource Polysynthetic Language<\/a><\/h3>

V. Gupta and G. Boulianne, “Automatic Transcription Challenges for Inuktitut, a Low-Resource Polysynthetic Language”, in Proceedings of the 12th Language Resources and Evaluation Conference (LREC 2020), pp. 2521-2527, May, 2020.<\/p>

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On the Performance of Time-Pooling Strategies for End-to-End Spoken Language Identification<\/a><\/h3>

J. Montiero, J. Alam, and T. Falk, “On the Performance of Time-Pooling Strategies for End-to-End Spoken Language Identification” in Proceedings of the 12th Language Resources and Evaluation Conference (LREC 2020), pp. 3566-3572, May, 2020.<\/p>

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Speech Transcription Challenges for Resource Constrained Indigenous Language Cree<\/a><\/h3>

V. Gupta and G. Boulianne, “Speech Transcription Challenges for Resource Constrained Indigenous Language Cree”, in Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL) @LREC 2020, pp. 362-367, May, 2020.<\/p>

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A Multimodal Non-Intrusive Stress Monitoring from the Pleasure-Arousal Emotional Dimensions<\/a><\/h3>

M. Dahmane, J. Alam, P. St-Charles, M. Lalonde, K. Heffner and S. Foucher, “A Multimodal Non-Intrusive Stress Monitoring from the Pleasure-Arousal Emotional Dimensions,” in IEEE Transactions on Affective Computing, April 20, 2020.<\/p>

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Application of convolutional neural networks to the analysis of polarimetric radar images in urban environments<\/a><\/h3>

M. Beaulieu, “Application des r\u00e9seaux neuronaux \u00e0 convolution \u00e0 l’analyse des images radar polarim\u00e9triques en milieu urbain”. Master of Science (M.Sc.) thesis in Geography, Universit\u00e9 de Montr\u00e9al, 2020.<\/p>

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Partly Uncoupled Siamese Model for Change Detection from Heterogeneous Remote Sensing Imagery<\/a><\/h3>

R. Touati, M. Mignotte, and M. Dahmane, “Partly Uncoupled Siamese Model for Change Detection from Heterogeneous Remote Sensing Imagery” in Journal of Remote Sensing & GIS, Volume 9, (1), pp. 272, March, 2020.<\/p>

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Anomaly Feature Learning for Unsupervised Change Detection in Heterogeneous Images: A Deep Sparse Residual Model<\/a><\/h3>

R. Touati, M. Mignotte, and M. Dahmane, “Anomaly Feature Learning for Unsupervised Change Detection in Heterogeneous Images: A Deep Sparse Residual Model”, in IEEE Journal of Selected Topics in Applied Earth Observations Remote Sensing, Volume 13, pp. 588-600, January 22, 2020.<\/p>

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TousActe de conf\u00e9renceLivreP\u00e9riodiqueRapport techniqueTh\u00e8se \/ M\u00e9moire ABC System Description for NIST SRE 2021 Alam, J. et al., “ABC System Description for NIST SRE 2021”, NIST SRE 2021 Workshop, 14-16 d\u00e9cembre 2021. Investigation on Instance Mixup Regularization Strategies for Self-supervised Speaker Representation Learning Kang, W., Alam, J., Fathan, A., “Investigation on Instance Mixup Regularization Strategies for […]<\/p>\n","protected":false},"author":34,"featured_media":14913,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-19361","page","type-page","status-publish","has-post-thumbnail","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.crim.ca\/en\/wp-json\/wp\/v2\/pages\/19361","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.crim.ca\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.crim.ca\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.crim.ca\/en\/wp-json\/wp\/v2\/users\/34"}],"replies":[{"embeddable":true,"href":"https:\/\/www.crim.ca\/en\/wp-json\/wp\/v2\/comments?post=19361"}],"version-history":[{"count":4,"href":"https:\/\/www.crim.ca\/en\/wp-json\/wp\/v2\/pages\/19361\/revisions"}],"predecessor-version":[{"id":28315,"href":"https:\/\/www.crim.ca\/en\/wp-json\/wp\/v2\/pages\/19361\/revisions\/28315"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.crim.ca\/en\/wp-json\/wp\/v2\/media\/14913"}],"wp:attachment":[{"href":"https:\/\/www.crim.ca\/en\/wp-json\/wp\/v2\/media?parent=19361"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}