Large-scale optimization of hierarchical features for saliency prediction in natural images

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2014, Pages 2798-2805

  Vig, Eleonora ^a,c   Dorr, Michael ^b   Cox, David ^a  

^a HARVARD UNIVERSITY   (United States)

^b HARVARD MEDICAL SCHOOL   (United States)

^c Xerox Research Centre Europe   (United States)

Author keywords

deep learning; hyperparameter optimization; saliency

Indexed keywords

BENCHMARKING; BIOMIMETICS; DEEP LEARNING; FORECASTING; PATTERN RECOGNITION;

BIO-INSPIRED MODELS; DATA-DRIVEN APPROACH; HIERARCHICAL FEATURES; HIGH DIMENSIONALITY; HYPER-PARAMETER OPTIMIZATIONS; LARGE-SCALE OPTIMIZATION; NEUROMORPHIC NETWORKS; SALIENCY;

CLASSIFICATION (OF INFORMATION);

EID: 84911369162     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2014.358     Document Type: Conference Paper

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