Imperial College London

DrCarloCiliberto

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 6173c.ciliberto CV

 
 
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Location

 

1003Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Pasquale:2016:10.1109/IROS.2016.7759720,
author = {Pasquale, G and Ciliberto, C and Rosasco, L and Natale, L},
doi = {10.1109/IROS.2016.7759720},
pages = {4904--4911},
title = {Object identification from few examples by improving the invariance of a deep convolutional neural network},
url = {http://dx.doi.org/10.1109/IROS.2016.7759720},
year = {2016}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - © 2016 IEEE. The development of reliable and robust visual recognition systems is a main challenge towards the deployment of autonomous robotic agents in unconstrained environments. Learning to recognize objects requires image representations that are discriminative to relevant information while being invariant to nuisances, such as scaling, rotations, light and background changes, and so forth. Deep Convolutional Neural Networks can learn such representations from large webcollected image datasets and a natural question is how these systems can be best adapted to the robotics context where little supervision is often available. In this work, we investigate different training strategies for deep architectures on a new dataset collected in a real-world robotic setting. In particular we show how deep networks can be tuned to improve invariance and discriminability properties and perform object identification tasks with minimal supervision.
AU - Pasquale,G
AU - Ciliberto,C
AU - Rosasco,L
AU - Natale,L
DO - 10.1109/IROS.2016.7759720
EP - 4911
PY - 2016///
SN - 2153-0858
SP - 4904
TI - Object identification from few examples by improving the invariance of a deep convolutional neural network
UR - http://dx.doi.org/10.1109/IROS.2016.7759720
ER -