TA1.L3.2
FEATURES DISENTANGLEMENT FOR EXPLAINABLE CONVOLUTIONAL NEURAL NETWORKS
Pasquale Coscia, Angelo Genovese, Fabio Scotti, Vincenzo Piuri, Università degli Studi di Milano, Italy
Session:
TA1.L3: Applications of Explainable AI Lecture
Track:
Visual Artificial Intelligence
Location:
Lecture Room 3
Presentation Time:
Tue, 29 Oct, 08:48 - 09:06 Gulf Standard Time (UTC +4)
Session Chair:
Jenni Raitoharju,
Session TA1.L3
TA1.L3.1: EXPLAINING 3D OBJECT DETECTION THROUGH SHAPLEY VALUE-BASED ATTRIBUTION MAP
Michihiro Kuroki, Toshihiko Yamasaki, The University of Tokyo, Japan
TA1.L3.2: FEATURES DISENTANGLEMENT FOR EXPLAINABLE CONVOLUTIONAL NEURAL NETWORKS
Pasquale Coscia, Angelo Genovese, Fabio Scotti, Vincenzo Piuri, Università degli Studi di Milano, Italy
TA1.L3.3: EMBEDDING ATTENTION BLOCKS FOR ANSWER GROUNDING
Seyedalireza Khoshsirat, Chandra Kambhamettu, University of Delaware, United States of America
TA1.L3.4: ENHANCED PROTOTYPICAL PART NETWORK (EPPNET) FOR EXPLAINABLE IMAGE CLASSIFICATION VIA PROTOTYPES
Bhushan Atote, Student, United Kingdom of Great Britain and Northern Ireland; Victor Sanchez, Professor, United Kingdom of Great Britain and Northern Ireland
TA1.L3.5: INTERACTIVE TEACHING FOR FINE-GRANULAR FEW-SHOT OBJECT RECOGNITION USING VISION TRANSFORMERS
Philip Keller, FZI Research Center for Information Technology, Germany; Daniel Jost, Karlsruhe Institute of Technology, Germany; Arne Roennau, Rüdiger Dillmann, FZI Research Center for Information Technology, Germany
Contacts