TA.L306.5
Purifying Adversarial Images using Adversarial Autoencoders with Conditional Normalizing Flows
Yi Ji, University of Tokyo; Trung-Nghia Le, National Institute of Informatics; Huy Nguyen, Graduate University for Advanced Studies; Isao Echizen, National Institute of Informatics
Session:
TA.L306: Adversarial Learning for Image Processing Lecture
Track:
Applications of Machine Learning
Location:
Room 306
Presentation Time:
Tue, 10 Oct, 12:12 - 12:30 Malaysia Time (UTC +8)
Session Chair:
William Puech, Université de Montpellier, FRANCE
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Session TA.L306
TA.L306.1: EXPLORING THE CONNECTION BETWEEN NEURON COVERAGE AND ADVERSARIAL ROBUSTNESS IN DNN CLASSIFIERS
William Piat, Safran Tech / Univ. Caen, France; Jalal Fadili, École nationale supérieure d'ingénieurs de Caen - ENSICAEN, France; Frédéric Jurie, University of Caen Normandie, France
TA.L306.2: ADVERSARIAL EXAMPLE DETECTION BAYESIAN GAME
Hui Zeng, Southwest university of science and technology, China; Biwei Chen, Beijing Normal University, China; Kang Deng, Anjie Peng, Southwest University of Science and Technology, China
TA.L306.3: DATA GENERATION WITH STRUCTURE ENFORCING ADVERSARIAL LEARNING
Indu Solomon, Uttam Kumar, International Institute of Information Technology Bangalore, India; Senthilnath Jayavelu, Institute for Infocomm Research, Agency for Science Technology and Research (A*STAR), Singapore
TA.L306.4: A PENALIZED MODIFIED HUBER REGULARIZATION TO IMPROVE ADVERSARIAL ROBUSTNESS
Modeste Atsague, Ashutosh Nirala, Olukorede Fakorede, Jin Tian, Iowa State University, United States of America
TA.L306.5: Purifying Adversarial Images using Adversarial Autoencoders with Conditional Normalizing Flows
Yi Ji, University of Tokyo; Trung-Nghia Le, National Institute of Informatics; Huy Nguyen, Graduate University for Advanced Studies; Isao Echizen, National Institute of Informatics
Contacts