TY - GEN
T1 - Deep active learning for image classification
AU - Ranganathan, Hiranmayi
AU - Demakethepalli Venkateswara, Hemanth
AU - Chakraborty, Shayok
AU - Panchanathan, Sethuraman
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - In the recent years, deep learning algorithms have achieved state-of-the-art performance in a variety of computer vision applications. In this paper, we propose a novel active learning framework to select the most informative unlabeled samples to train a deep belief network model. We introduce a loss function specific to the active learning task and train the model to minimize the loss function. To the best of our knowledge, this is the first research effort to integrate an active learning based criterion in the loss function used to train a deep belief network. Our extensive empirical studies on a wide variety of uni-modal and multi-modal vision datasets corroborate the potential of the method for real-world image recognition applications.
AB - In the recent years, deep learning algorithms have achieved state-of-the-art performance in a variety of computer vision applications. In this paper, we propose a novel active learning framework to select the most informative unlabeled samples to train a deep belief network model. We introduce a loss function specific to the active learning task and train the model to minimize the loss function. To the best of our knowledge, this is the first research effort to integrate an active learning based criterion in the loss function used to train a deep belief network. Our extensive empirical studies on a wide variety of uni-modal and multi-modal vision datasets corroborate the potential of the method for real-world image recognition applications.
KW - Active learning
KW - Computer vision
KW - Deep belief networks
KW - Deep learning
KW - Entropy
UR - http://www.scopus.com/inward/record.url?scp=85045307496&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045307496&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2017.8297020
DO - 10.1109/ICIP.2017.8297020
M3 - Conference contribution
AN - SCOPUS:85045307496
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 3934
EP - 3938
BT - 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PB - IEEE Computer Society
T2 - 24th IEEE International Conference on Image Processing, ICIP 2017
Y2 - 17 September 2017 through 20 September 2017
ER -