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Neural network learning (MLR-NNLR)

LEARNING TO GENERATE IMAGES WITH PERCEPTUAL SIMILARITY METRICS (POSTER)

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Authors:
Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel
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14 September 2017 - 10:45pm
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[1] Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel, "LEARNING TO GENERATE IMAGES WITH PERCEPTUAL SIMILARITY METRICS (POSTER)", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2073. Accessed: Jul. 20, 2019.
@article{2073-17,
url = {http://sigport.org/2073},
author = {Jake Snell; Karl Ridgeway; Renjie Liao; Brett D. Roads; Michael C. Mozer; Richard S. Zemel },
publisher = {IEEE SigPort},
title = {LEARNING TO GENERATE IMAGES WITH PERCEPTUAL SIMILARITY METRICS (POSTER)},
year = {2017} }
TY - EJOUR
T1 - LEARNING TO GENERATE IMAGES WITH PERCEPTUAL SIMILARITY METRICS (POSTER)
AU - Jake Snell; Karl Ridgeway; Renjie Liao; Brett D. Roads; Michael C. Mozer; Richard S. Zemel
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2073
ER -
Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel. (2017). LEARNING TO GENERATE IMAGES WITH PERCEPTUAL SIMILARITY METRICS (POSTER). IEEE SigPort. http://sigport.org/2073
Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel, 2017. LEARNING TO GENERATE IMAGES WITH PERCEPTUAL SIMILARITY METRICS (POSTER). Available at: http://sigport.org/2073.
Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel. (2017). "LEARNING TO GENERATE IMAGES WITH PERCEPTUAL SIMILARITY METRICS (POSTER)." Web.
1. Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel. LEARNING TO GENERATE IMAGES WITH PERCEPTUAL SIMILARITY METRICS (POSTER) [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2073

PERSON RE-IDENTIFICATION USING VISUAL ATTENTION


Despite recent attempts for solving the person re-identification problem, it remains a challenging task since a person’s appearance can vary significantly when large variations in view angle, human pose and illumination are involved. The concept of attention is one of the most interesting recent architectural innovations in neural networks. Inspired by that, in this paper we propose a novel approach based on using a gradient-based attention mechanism in deep convolution neural network for solving the person re-identification problem.

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Authors:
Hairong Qi
Submitted On:
14 September 2017 - 4:12pm
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Poster

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[1] Hairong Qi, "PERSON RE-IDENTIFICATION USING VISUAL ATTENTION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2046. Accessed: Jul. 20, 2019.
@article{2046-17,
url = {http://sigport.org/2046},
author = {Hairong Qi },
publisher = {IEEE SigPort},
title = {PERSON RE-IDENTIFICATION USING VISUAL ATTENTION},
year = {2017} }
TY - EJOUR
T1 - PERSON RE-IDENTIFICATION USING VISUAL ATTENTION
AU - Hairong Qi
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2046
ER -
Hairong Qi. (2017). PERSON RE-IDENTIFICATION USING VISUAL ATTENTION. IEEE SigPort. http://sigport.org/2046
Hairong Qi, 2017. PERSON RE-IDENTIFICATION USING VISUAL ATTENTION. Available at: http://sigport.org/2046.
Hairong Qi. (2017). "PERSON RE-IDENTIFICATION USING VISUAL ATTENTION." Web.
1. Hairong Qi. PERSON RE-IDENTIFICATION USING VISUAL ATTENTION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2046

MOTION BLUR REMOVAL VIA COUPLED AUTOENCODER

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Authors:
Brojeshwar Bhwomick , Angshul Majumdar
Submitted On:
14 September 2017 - 7:04am
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[1] Brojeshwar Bhwomick , Angshul Majumdar, "MOTION BLUR REMOVAL VIA COUPLED AUTOENCODER", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2014. Accessed: Jul. 20, 2019.
@article{2014-17,
url = {http://sigport.org/2014},
author = {Brojeshwar Bhwomick ; Angshul Majumdar },
publisher = {IEEE SigPort},
title = {MOTION BLUR REMOVAL VIA COUPLED AUTOENCODER},
year = {2017} }
TY - EJOUR
T1 - MOTION BLUR REMOVAL VIA COUPLED AUTOENCODER
AU - Brojeshwar Bhwomick ; Angshul Majumdar
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2014
ER -
Brojeshwar Bhwomick , Angshul Majumdar. (2017). MOTION BLUR REMOVAL VIA COUPLED AUTOENCODER. IEEE SigPort. http://sigport.org/2014
Brojeshwar Bhwomick , Angshul Majumdar, 2017. MOTION BLUR REMOVAL VIA COUPLED AUTOENCODER. Available at: http://sigport.org/2014.
Brojeshwar Bhwomick , Angshul Majumdar. (2017). "MOTION BLUR REMOVAL VIA COUPLED AUTOENCODER." Web.
1. Brojeshwar Bhwomick , Angshul Majumdar. MOTION BLUR REMOVAL VIA COUPLED AUTOENCODER [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2014

LEARNING AUTOENCODERS WITH LOW-RANK WEIGHTS

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Authors:
Angshul Majumdar
Submitted On:
14 September 2017 - 7:00am
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Poster for ICIP Kavya_1x2M_120917_Prepress.pdf

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[1] Angshul Majumdar, "LEARNING AUTOENCODERS WITH LOW-RANK WEIGHTS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2013. Accessed: Jul. 20, 2019.
@article{2013-17,
url = {http://sigport.org/2013},
author = {Angshul Majumdar },
publisher = {IEEE SigPort},
title = {LEARNING AUTOENCODERS WITH LOW-RANK WEIGHTS},
year = {2017} }
TY - EJOUR
T1 - LEARNING AUTOENCODERS WITH LOW-RANK WEIGHTS
AU - Angshul Majumdar
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2013
ER -
Angshul Majumdar. (2017). LEARNING AUTOENCODERS WITH LOW-RANK WEIGHTS. IEEE SigPort. http://sigport.org/2013
Angshul Majumdar, 2017. LEARNING AUTOENCODERS WITH LOW-RANK WEIGHTS. Available at: http://sigport.org/2013.
Angshul Majumdar. (2017). "LEARNING AUTOENCODERS WITH LOW-RANK WEIGHTS." Web.
1. Angshul Majumdar. LEARNING AUTOENCODERS WITH LOW-RANK WEIGHTS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2013

UNDERSTANDING NEURAL-NETWORK DENOISERS THROUGH AN ACTIVATION FUNCTION PERSPECTIVE

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Authors:
Yuxiang Li, Bo Zhang, Raoul Florent
Submitted On:
14 September 2017 - 6:38am
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[1] Yuxiang Li, Bo Zhang, Raoul Florent, "UNDERSTANDING NEURAL-NETWORK DENOISERS THROUGH AN ACTIVATION FUNCTION PERSPECTIVE", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2009. Accessed: Jul. 20, 2019.
@article{2009-17,
url = {http://sigport.org/2009},
author = {Yuxiang Li; Bo Zhang; Raoul Florent },
publisher = {IEEE SigPort},
title = {UNDERSTANDING NEURAL-NETWORK DENOISERS THROUGH AN ACTIVATION FUNCTION PERSPECTIVE},
year = {2017} }
TY - EJOUR
T1 - UNDERSTANDING NEURAL-NETWORK DENOISERS THROUGH AN ACTIVATION FUNCTION PERSPECTIVE
AU - Yuxiang Li; Bo Zhang; Raoul Florent
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2009
ER -
Yuxiang Li, Bo Zhang, Raoul Florent. (2017). UNDERSTANDING NEURAL-NETWORK DENOISERS THROUGH AN ACTIVATION FUNCTION PERSPECTIVE. IEEE SigPort. http://sigport.org/2009
Yuxiang Li, Bo Zhang, Raoul Florent, 2017. UNDERSTANDING NEURAL-NETWORK DENOISERS THROUGH AN ACTIVATION FUNCTION PERSPECTIVE. Available at: http://sigport.org/2009.
Yuxiang Li, Bo Zhang, Raoul Florent. (2017). "UNDERSTANDING NEURAL-NETWORK DENOISERS THROUGH AN ACTIVATION FUNCTION PERSPECTIVE." Web.
1. Yuxiang Li, Bo Zhang, Raoul Florent. UNDERSTANDING NEURAL-NETWORK DENOISERS THROUGH AN ACTIVATION FUNCTION PERSPECTIVE [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2009

Multi Layer Multi Objective Extreme Learning Machine


Fully connected multi layer neural networks such as Deep Boltzmann Machines (DBM) performs better than fully connected single layer neural networks in image classification tasks and has a smaller number of hidden layer neurons than Extreme Learning Machine (ELM) based fully connected multi layer neural networks such as Multi Layer ELM (ML-ELM) and Hierarchical ELM (H-ELM) However, ML-ELM and H-ELM has a smaller training time than DBM.

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Authors:
Chamara Kasun Liyanaarachchi Lekamalage, Kang Song, Guang-Bin Huang, Dongshun Cui and Ken Liang
Submitted On:
12 September 2017 - 11:22pm
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MLMO_ELM_presentation.pdf

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[1] Chamara Kasun Liyanaarachchi Lekamalage, Kang Song, Guang-Bin Huang, Dongshun Cui and Ken Liang, "Multi Layer Multi Objective Extreme Learning Machine", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1957. Accessed: Jul. 20, 2019.
@article{1957-17,
url = {http://sigport.org/1957},
author = {Chamara Kasun Liyanaarachchi Lekamalage; Kang Song; Guang-Bin Huang; Dongshun Cui and Ken Liang },
publisher = {IEEE SigPort},
title = {Multi Layer Multi Objective Extreme Learning Machine},
year = {2017} }
TY - EJOUR
T1 - Multi Layer Multi Objective Extreme Learning Machine
AU - Chamara Kasun Liyanaarachchi Lekamalage; Kang Song; Guang-Bin Huang; Dongshun Cui and Ken Liang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1957
ER -
Chamara Kasun Liyanaarachchi Lekamalage, Kang Song, Guang-Bin Huang, Dongshun Cui and Ken Liang. (2017). Multi Layer Multi Objective Extreme Learning Machine. IEEE SigPort. http://sigport.org/1957
Chamara Kasun Liyanaarachchi Lekamalage, Kang Song, Guang-Bin Huang, Dongshun Cui and Ken Liang, 2017. Multi Layer Multi Objective Extreme Learning Machine. Available at: http://sigport.org/1957.
Chamara Kasun Liyanaarachchi Lekamalage, Kang Song, Guang-Bin Huang, Dongshun Cui and Ken Liang. (2017). "Multi Layer Multi Objective Extreme Learning Machine." Web.
1. Chamara Kasun Liyanaarachchi Lekamalage, Kang Song, Guang-Bin Huang, Dongshun Cui and Ken Liang. Multi Layer Multi Objective Extreme Learning Machine [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1957

Residual Networks of Residual Networks: Multilevel Residual Networks


A residual-networks family with hundreds or even thousands of layers dominates major image recognition tasks, but building a network by simply stacking residual blocks inevitably limits its optimization ability. This paper proposes a novel residual-network architecture, Residual networks of Residual networks (RoR), to dig the optimization ability of residual networks. RoR substitutes optimizing residual mapping of residual mapping for optimizing original residual mapping.

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Authors:
Ke Zhang, Miao Sun, Tony Han, Xingfang Yuan, Liru Guo, Tao Liu
Submitted On:
12 September 2017 - 10:28pm
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[1] Ke Zhang, Miao Sun, Tony Han, Xingfang Yuan, Liru Guo, Tao Liu, "Residual Networks of Residual Networks: Multilevel Residual Networks", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1954. Accessed: Jul. 20, 2019.
@article{1954-17,
url = {http://sigport.org/1954},
author = {Ke Zhang; Miao Sun; Tony Han; Xingfang Yuan; Liru Guo; Tao Liu },
publisher = {IEEE SigPort},
title = {Residual Networks of Residual Networks: Multilevel Residual Networks},
year = {2017} }
TY - EJOUR
T1 - Residual Networks of Residual Networks: Multilevel Residual Networks
AU - Ke Zhang; Miao Sun; Tony Han; Xingfang Yuan; Liru Guo; Tao Liu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1954
ER -
Ke Zhang, Miao Sun, Tony Han, Xingfang Yuan, Liru Guo, Tao Liu. (2017). Residual Networks of Residual Networks: Multilevel Residual Networks. IEEE SigPort. http://sigport.org/1954
Ke Zhang, Miao Sun, Tony Han, Xingfang Yuan, Liru Guo, Tao Liu, 2017. Residual Networks of Residual Networks: Multilevel Residual Networks. Available at: http://sigport.org/1954.
Ke Zhang, Miao Sun, Tony Han, Xingfang Yuan, Liru Guo, Tao Liu. (2017). "Residual Networks of Residual Networks: Multilevel Residual Networks." Web.
1. Ke Zhang, Miao Sun, Tony Han, Xingfang Yuan, Liru Guo, Tao Liu. Residual Networks of Residual Networks: Multilevel Residual Networks [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1954

Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework


In this paper, we introduce an adaptive unsupervised learning framework, which utilizes natural images to train filter sets. The ap- plicability of these filter sets is demonstrated by evaluating their per- formance in two contrasting applications - image quality assessment and texture retrieval. While assessing image quality, the filters need to capture perceptual differences based on dissimilarities between a reference image and its distorted version. In texture retrieval, the filters need to assess similarity between texture images to retrieve closest matching textures.

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Authors:
Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib
Submitted On:
11 September 2017 - 6:47pm
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[1] Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib, "Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1921. Accessed: Jul. 20, 2019.
@article{1921-17,
url = {http://sigport.org/1921},
author = {Mohit Prabhushankar; Dogancan Temel; and Ghassan Alregib },
publisher = {IEEE SigPort},
title = {Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework},
year = {2017} }
TY - EJOUR
T1 - Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework
AU - Mohit Prabhushankar; Dogancan Temel; and Ghassan Alregib
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1921
ER -
Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib. (2017). Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework. IEEE SigPort. http://sigport.org/1921
Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib, 2017. Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework. Available at: http://sigport.org/1921.
Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib. (2017). "Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework." Web.
1. Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib. Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1921

Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework


In this paper, we introduce an adaptive unsupervised learning framework, which utilizes natural images to train filter sets. The ap- plicability of these filter sets is demonstrated by evaluating their per- formance in two contrasting applications - image quality assessment and texture retrieval. While assessing image quality, the filters need to capture perceptual differences based on dissimilarities between a reference image and its distorted version. In texture retrieval, the filters need to assess similarity between texture images to retrieve closest matching textures.

Paper Details

Authors:
Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib
Submitted On:
11 September 2017 - 6:47pm
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ICIP2017Poster_UnsupervisedFramework_MohitCan.pdf

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[1] Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib, "Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1920. Accessed: Jul. 20, 2019.
@article{1920-17,
url = {http://sigport.org/1920},
author = {Mohit Prabhushankar; Dogancan Temel; and Ghassan Alregib },
publisher = {IEEE SigPort},
title = {Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework},
year = {2017} }
TY - EJOUR
T1 - Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework
AU - Mohit Prabhushankar; Dogancan Temel; and Ghassan Alregib
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1920
ER -
Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib. (2017). Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework. IEEE SigPort. http://sigport.org/1920
Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib, 2017. Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework. Available at: http://sigport.org/1920.
Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib. (2017). "Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework." Web.
1. Mohit Prabhushankar, Dogancan Temel, and Ghassan Alregib. Generating Adaptive and Robust Filter Sets using an Unsupervised Learning Framework [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1920

Learning a Cross-Modal Hashing Network for Multimedia Search


In this paper, we propose a cross-modal hashing network (CMHN) method to learn compact binary codes for cross-modality multimedia search. Unlike most existing cross-modal hashing methods which learn a single pair of projections to map each example into a binary vector, we design a deep neural network to learn multiple pairs of hierarchical non-linear transformations, under which the nonlinear characteristics of samples can be well exploited and the modality gap is well reduced.

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Authors:
Venice Erin Liong, Jiwen Lu, Yap-Peng Tan
Submitted On:
11 September 2017 - 5:44am
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icip2017_poster_2555.pdf

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[1] Venice Erin Liong, Jiwen Lu, Yap-Peng Tan, "Learning a Cross-Modal Hashing Network for Multimedia Search", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1901. Accessed: Jul. 20, 2019.
@article{1901-17,
url = {http://sigport.org/1901},
author = {Venice Erin Liong; Jiwen Lu; Yap-Peng Tan },
publisher = {IEEE SigPort},
title = {Learning a Cross-Modal Hashing Network for Multimedia Search},
year = {2017} }
TY - EJOUR
T1 - Learning a Cross-Modal Hashing Network for Multimedia Search
AU - Venice Erin Liong; Jiwen Lu; Yap-Peng Tan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1901
ER -
Venice Erin Liong, Jiwen Lu, Yap-Peng Tan. (2017). Learning a Cross-Modal Hashing Network for Multimedia Search. IEEE SigPort. http://sigport.org/1901
Venice Erin Liong, Jiwen Lu, Yap-Peng Tan, 2017. Learning a Cross-Modal Hashing Network for Multimedia Search. Available at: http://sigport.org/1901.
Venice Erin Liong, Jiwen Lu, Yap-Peng Tan. (2017). "Learning a Cross-Modal Hashing Network for Multimedia Search." Web.
1. Venice Erin Liong, Jiwen Lu, Yap-Peng Tan. Learning a Cross-Modal Hashing Network for Multimedia Search [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1901

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