Caffe: Convolutional architecture for fast feature embedding

MM 2014 - Proceedings of the 2014 ACM Conference on Multimedia

Volumn , Issue , 2014, Pages 675-678

  Jia, Yangqing ^a   Shelhamer, Evan ^a,b   Donahue, Jeff ^a   Karayev, Sergey ^a   Long, Jonathan ^a   Girshick, Ross ^a   Guadarrama, Sergio ^a   Darrell, Trevor ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b GOOGLE INC   (United States)

Author keywords

Computer vision; Machine learning; Neural networks; Open source; Parallel computation

Indexed keywords

C++ (PROGRAMMING LANGUAGE); COMPUTER VISION; CONVOLUTION; CONVOLUTIONAL NEURAL NETWORKS; DEEP LEARNING; GRAPHICS PROCESSING UNIT; INDUSTRIAL RESEARCH; LEARNING SYSTEMS; NETWORK ARCHITECTURE; NEURAL NETWORKS; OBJECT ORIENTED PROGRAMMING;

CLOUD ENVIRONMENTS; FEATURE EMBEDDING; OPEN SOURCES; PARALLEL COMPUTATION; REFERENCE MODELS; SEAMLESS SWITCHING; SEPARATING MODELS; STATE OF THE ART;

LEARNING ALGORITHMS;

EID: 84913580146     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2647868.2654889     Document Type: Conference Paper

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