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Machine Learning for Signal Processing

OUTLIER REMOVAL FOR ENHANCING KERNEL-BASED CLASSIFIER VIA THE DISCRIMINANT INFORMATION


Pattern recognition on big data can be challenging for kernel machines as the complexity grows with the squared number of training samples. In this work, we overcome this hurdle via the outlying data sample removal pre-processing step. This approach removes less informative data samples and trains the kernel machines only with the remaining data, and hence, directly reduces the complexity by reducing the number of training samples. To enhance the classification performance, the outlier removal process is done such that the discriminant information of the data is mostly intact.

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Authors:
Thee Chanyaswad, Mert Al, Sun-Yuan Kung
Submitted On:
14 April 2018 - 9:10pm
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[1] Thee Chanyaswad, Mert Al, Sun-Yuan Kung, "OUTLIER REMOVAL FOR ENHANCING KERNEL-BASED CLASSIFIER VIA THE DISCRIMINANT INFORMATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2754. Accessed: Oct. 16, 2019.
@article{2754-18,
url = {http://sigport.org/2754},
author = {Thee Chanyaswad; Mert Al; Sun-Yuan Kung },
publisher = {IEEE SigPort},
title = {OUTLIER REMOVAL FOR ENHANCING KERNEL-BASED CLASSIFIER VIA THE DISCRIMINANT INFORMATION},
year = {2018} }
TY - EJOUR
T1 - OUTLIER REMOVAL FOR ENHANCING KERNEL-BASED CLASSIFIER VIA THE DISCRIMINANT INFORMATION
AU - Thee Chanyaswad; Mert Al; Sun-Yuan Kung
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2754
ER -
Thee Chanyaswad, Mert Al, Sun-Yuan Kung. (2018). OUTLIER REMOVAL FOR ENHANCING KERNEL-BASED CLASSIFIER VIA THE DISCRIMINANT INFORMATION. IEEE SigPort. http://sigport.org/2754
Thee Chanyaswad, Mert Al, Sun-Yuan Kung, 2018. OUTLIER REMOVAL FOR ENHANCING KERNEL-BASED CLASSIFIER VIA THE DISCRIMINANT INFORMATION. Available at: http://sigport.org/2754.
Thee Chanyaswad, Mert Al, Sun-Yuan Kung. (2018). "OUTLIER REMOVAL FOR ENHANCING KERNEL-BASED CLASSIFIER VIA THE DISCRIMINANT INFORMATION." Web.
1. Thee Chanyaswad, Mert Al, Sun-Yuan Kung. OUTLIER REMOVAL FOR ENHANCING KERNEL-BASED CLASSIFIER VIA THE DISCRIMINANT INFORMATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2754

ADAPTIVE CLUSTERING ALGORITHM FOR COOPERATIVE SPECTRUM SENSING IN MOBILE ENVIRONMENTS


In this work we propose a new adaptive algorithm for coop- erative spectrum sensing in dynamic environments where the channels are time varying. We assume a centralized spectrum sensing procedure based on the soft fusion of the signal energy levels measured at the sensors. The detection problem is posed as a composite hypothesis testing problem. The unknown pa- rameters are estimated by means of an adaptive clustering al- gorithm that operates over the most recent energy estimates re- ported by the sensors to the fusion center.

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Authors:
Ignacio Santamaria
Submitted On:
13 April 2018 - 2:41pm
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[1] Ignacio Santamaria, "ADAPTIVE CLUSTERING ALGORITHM FOR COOPERATIVE SPECTRUM SENSING IN MOBILE ENVIRONMENTS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2730. Accessed: Oct. 16, 2019.
@article{2730-18,
url = {http://sigport.org/2730},
author = {Ignacio Santamaria },
publisher = {IEEE SigPort},
title = {ADAPTIVE CLUSTERING ALGORITHM FOR COOPERATIVE SPECTRUM SENSING IN MOBILE ENVIRONMENTS},
year = {2018} }
TY - EJOUR
T1 - ADAPTIVE CLUSTERING ALGORITHM FOR COOPERATIVE SPECTRUM SENSING IN MOBILE ENVIRONMENTS
AU - Ignacio Santamaria
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2730
ER -
Ignacio Santamaria. (2018). ADAPTIVE CLUSTERING ALGORITHM FOR COOPERATIVE SPECTRUM SENSING IN MOBILE ENVIRONMENTS. IEEE SigPort. http://sigport.org/2730
Ignacio Santamaria, 2018. ADAPTIVE CLUSTERING ALGORITHM FOR COOPERATIVE SPECTRUM SENSING IN MOBILE ENVIRONMENTS. Available at: http://sigport.org/2730.
Ignacio Santamaria. (2018). "ADAPTIVE CLUSTERING ALGORITHM FOR COOPERATIVE SPECTRUM SENSING IN MOBILE ENVIRONMENTS." Web.
1. Ignacio Santamaria. ADAPTIVE CLUSTERING ALGORITHM FOR COOPERATIVE SPECTRUM SENSING IN MOBILE ENVIRONMENTS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2730

Cross-Modality Distillation: A Case for Conditional Generative Adversarial Networks


In this paper, we propose to use a Conditional Generative Adversarial Network (CGAN) for distilling (i.e. transferring) knowledge from sensor data and enhancing low-resolution target detection. In unconstrained surveillance settings, sensor measurements are often noisy, degraded, corrupted, and even missing/absent, thereby presenting a significant problem for multi-modal fusion. We therefore specifically tackle the problem of a missing modality in our attempt to propose an algorithm

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Authors:
Siddharth Roheda, Benjamin S Riggan, Hamid Krim, Liyi Dai
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13 April 2018 - 12:56pm
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[1] Siddharth Roheda, Benjamin S Riggan, Hamid Krim, Liyi Dai, "Cross-Modality Distillation: A Case for Conditional Generative Adversarial Networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2729. Accessed: Oct. 16, 2019.
@article{2729-18,
url = {http://sigport.org/2729},
author = {Siddharth Roheda; Benjamin S Riggan; Hamid Krim; Liyi Dai },
publisher = {IEEE SigPort},
title = {Cross-Modality Distillation: A Case for Conditional Generative Adversarial Networks},
year = {2018} }
TY - EJOUR
T1 - Cross-Modality Distillation: A Case for Conditional Generative Adversarial Networks
AU - Siddharth Roheda; Benjamin S Riggan; Hamid Krim; Liyi Dai
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2729
ER -
Siddharth Roheda, Benjamin S Riggan, Hamid Krim, Liyi Dai. (2018). Cross-Modality Distillation: A Case for Conditional Generative Adversarial Networks. IEEE SigPort. http://sigport.org/2729
Siddharth Roheda, Benjamin S Riggan, Hamid Krim, Liyi Dai, 2018. Cross-Modality Distillation: A Case for Conditional Generative Adversarial Networks. Available at: http://sigport.org/2729.
Siddharth Roheda, Benjamin S Riggan, Hamid Krim, Liyi Dai. (2018). "Cross-Modality Distillation: A Case for Conditional Generative Adversarial Networks." Web.
1. Siddharth Roheda, Benjamin S Riggan, Hamid Krim, Liyi Dai. Cross-Modality Distillation: A Case for Conditional Generative Adversarial Networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2729

DIRECTLY SOLVING THE ORIGINAL RATIOCUT PROBLEM FOR EFFECTIVE DATA CLUSTERING

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Authors:
Jing Li, Feiping Nie, Xuelong Li
Submitted On:
13 April 2018 - 10:58am
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[1] Jing Li, Feiping Nie, Xuelong Li, "DIRECTLY SOLVING THE ORIGINAL RATIOCUT PROBLEM FOR EFFECTIVE DATA CLUSTERING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2714. Accessed: Oct. 16, 2019.
@article{2714-18,
url = {http://sigport.org/2714},
author = {Jing Li; Feiping Nie; Xuelong Li },
publisher = {IEEE SigPort},
title = {DIRECTLY SOLVING THE ORIGINAL RATIOCUT PROBLEM FOR EFFECTIVE DATA CLUSTERING},
year = {2018} }
TY - EJOUR
T1 - DIRECTLY SOLVING THE ORIGINAL RATIOCUT PROBLEM FOR EFFECTIVE DATA CLUSTERING
AU - Jing Li; Feiping Nie; Xuelong Li
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2714
ER -
Jing Li, Feiping Nie, Xuelong Li. (2018). DIRECTLY SOLVING THE ORIGINAL RATIOCUT PROBLEM FOR EFFECTIVE DATA CLUSTERING. IEEE SigPort. http://sigport.org/2714
Jing Li, Feiping Nie, Xuelong Li, 2018. DIRECTLY SOLVING THE ORIGINAL RATIOCUT PROBLEM FOR EFFECTIVE DATA CLUSTERING. Available at: http://sigport.org/2714.
Jing Li, Feiping Nie, Xuelong Li. (2018). "DIRECTLY SOLVING THE ORIGINAL RATIOCUT PROBLEM FOR EFFECTIVE DATA CLUSTERING." Web.
1. Jing Li, Feiping Nie, Xuelong Li. DIRECTLY SOLVING THE ORIGINAL RATIOCUT PROBLEM FOR EFFECTIVE DATA CLUSTERING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2714

Regressing Kernel Dictionary Learning


In this paper, we present a kernelized dictionary learning framework for carrying out regression to model signals having a complex non-linear nature. A joint optimization is carried out where the regression weights are learnt together with the dictionary and coefficients. Relevant formulation and dictionary building steps are provided. To demonstrate the effectiveness of the proposed technique, elaborate experimental results using different real-life datasets are presented.

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25 April 2018 - 5:20am
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[1] , "Regressing Kernel Dictionary Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2711. Accessed: Oct. 16, 2019.
@article{2711-18,
url = {http://sigport.org/2711},
author = { },
publisher = {IEEE SigPort},
title = {Regressing Kernel Dictionary Learning},
year = {2018} }
TY - EJOUR
T1 - Regressing Kernel Dictionary Learning
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2711
ER -
. (2018). Regressing Kernel Dictionary Learning. IEEE SigPort. http://sigport.org/2711
, 2018. Regressing Kernel Dictionary Learning. Available at: http://sigport.org/2711.
. (2018). "Regressing Kernel Dictionary Learning." Web.
1. . Regressing Kernel Dictionary Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2711

LOCALITY-PRESERVING COMPLEX-VALUED GAUSSIAN PROCESS LATENT VARIABLE MODEL FOR ROBUST FACE RECOGNITION

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13 April 2018 - 2:03am
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[1] , "LOCALITY-PRESERVING COMPLEX-VALUED GAUSSIAN PROCESS LATENT VARIABLE MODEL FOR ROBUST FACE RECOGNITION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2617. Accessed: Oct. 16, 2019.
@article{2617-18,
url = {http://sigport.org/2617},
author = { },
publisher = {IEEE SigPort},
title = {LOCALITY-PRESERVING COMPLEX-VALUED GAUSSIAN PROCESS LATENT VARIABLE MODEL FOR ROBUST FACE RECOGNITION},
year = {2018} }
TY - EJOUR
T1 - LOCALITY-PRESERVING COMPLEX-VALUED GAUSSIAN PROCESS LATENT VARIABLE MODEL FOR ROBUST FACE RECOGNITION
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2617
ER -
. (2018). LOCALITY-PRESERVING COMPLEX-VALUED GAUSSIAN PROCESS LATENT VARIABLE MODEL FOR ROBUST FACE RECOGNITION. IEEE SigPort. http://sigport.org/2617
, 2018. LOCALITY-PRESERVING COMPLEX-VALUED GAUSSIAN PROCESS LATENT VARIABLE MODEL FOR ROBUST FACE RECOGNITION. Available at: http://sigport.org/2617.
. (2018). "LOCALITY-PRESERVING COMPLEX-VALUED GAUSSIAN PROCESS LATENT VARIABLE MODEL FOR ROBUST FACE RECOGNITION." Web.
1. . LOCALITY-PRESERVING COMPLEX-VALUED GAUSSIAN PROCESS LATENT VARIABLE MODEL FOR ROBUST FACE RECOGNITION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2617

Discriminative Probabilistic Framework for Generalized Multi-Instance Learning


Multiple-instance learning is a framework for learning from data consisting of bags of instances labeled at the bag level. A common assumption in multi-instance learning is that a bag label is positive if and only if at least one instance in the bag is positive. In practice, this assumption may be violated. For example, experts may provide a noisy label to a bag consisting of many instances, to reduce labeling time.

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Authors:
Anh T Pham, Raviv Raich, Xiaoli Fern; Weng K Wong; Xinze Guan
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25 April 2018 - 1:39pm
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[1] Anh T Pham, Raviv Raich, Xiaoli Fern; Weng K Wong; Xinze Guan, "Discriminative Probabilistic Framework for Generalized Multi-Instance Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2512. Accessed: Oct. 16, 2019.
@article{2512-18,
url = {http://sigport.org/2512},
author = {Anh T Pham; Raviv Raich; Xiaoli Fern; Weng K Wong; Xinze Guan },
publisher = {IEEE SigPort},
title = {Discriminative Probabilistic Framework for Generalized Multi-Instance Learning},
year = {2018} }
TY - EJOUR
T1 - Discriminative Probabilistic Framework for Generalized Multi-Instance Learning
AU - Anh T Pham; Raviv Raich; Xiaoli Fern; Weng K Wong; Xinze Guan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2512
ER -
Anh T Pham, Raviv Raich, Xiaoli Fern; Weng K Wong; Xinze Guan. (2018). Discriminative Probabilistic Framework for Generalized Multi-Instance Learning. IEEE SigPort. http://sigport.org/2512
Anh T Pham, Raviv Raich, Xiaoli Fern; Weng K Wong; Xinze Guan, 2018. Discriminative Probabilistic Framework for Generalized Multi-Instance Learning. Available at: http://sigport.org/2512.
Anh T Pham, Raviv Raich, Xiaoli Fern; Weng K Wong; Xinze Guan. (2018). "Discriminative Probabilistic Framework for Generalized Multi-Instance Learning." Web.
1. Anh T Pham, Raviv Raich, Xiaoli Fern; Weng K Wong; Xinze Guan. Discriminative Probabilistic Framework for Generalized Multi-Instance Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2512

Speaker-Invariant Training via Adversarial Learning


We propose a novel adversarial multi-task learning scheme, aiming at actively curtailing the inter-talker feature variability while maximizing its senone discriminability so as to enhance the performance of a deep neural network (DNN) based ASR system. We call the scheme speaker-invariant training (SIT). In SIT, a DNN acoustic model and a speaker classifier network are jointly optimized to minimize the senone (tied triphone state) classification loss, and simultaneously mini-maximize the speaker classification loss.

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Authors:
Zhong Meng, Jinyu Li, Zhuo Chen, Yong Zhao, Vadim Mazalov, Yifan Gong, Biing-Hwang (Fred) Juang
Submitted On:
12 May 2019 - 9:29pm
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[1] Zhong Meng, Jinyu Li, Zhuo Chen, Yong Zhao, Vadim Mazalov, Yifan Gong, Biing-Hwang (Fred) Juang, "Speaker-Invariant Training via Adversarial Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2508. Accessed: Oct. 16, 2019.
@article{2508-18,
url = {http://sigport.org/2508},
author = {Zhong Meng; Jinyu Li; Zhuo Chen; Yong Zhao; Vadim Mazalov; Yifan Gong; Biing-Hwang (Fred) Juang },
publisher = {IEEE SigPort},
title = {Speaker-Invariant Training via Adversarial Learning},
year = {2018} }
TY - EJOUR
T1 - Speaker-Invariant Training via Adversarial Learning
AU - Zhong Meng; Jinyu Li; Zhuo Chen; Yong Zhao; Vadim Mazalov; Yifan Gong; Biing-Hwang (Fred) Juang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2508
ER -
Zhong Meng, Jinyu Li, Zhuo Chen, Yong Zhao, Vadim Mazalov, Yifan Gong, Biing-Hwang (Fred) Juang. (2018). Speaker-Invariant Training via Adversarial Learning. IEEE SigPort. http://sigport.org/2508
Zhong Meng, Jinyu Li, Zhuo Chen, Yong Zhao, Vadim Mazalov, Yifan Gong, Biing-Hwang (Fred) Juang, 2018. Speaker-Invariant Training via Adversarial Learning. Available at: http://sigport.org/2508.
Zhong Meng, Jinyu Li, Zhuo Chen, Yong Zhao, Vadim Mazalov, Yifan Gong, Biing-Hwang (Fred) Juang. (2018). "Speaker-Invariant Training via Adversarial Learning." Web.
1. Zhong Meng, Jinyu Li, Zhuo Chen, Yong Zhao, Vadim Mazalov, Yifan Gong, Biing-Hwang (Fred) Juang. Speaker-Invariant Training via Adversarial Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2508

Adversarial Teacher-Student Learning for Unsupervised Adaptation


The teacher-student (T/S) learning has been shown effective in unsupervised domain adaptation ts_adapt. It is a form of transfer learning, not in terms of the transfer of recognition decisions, but the knowledge of posteriori probabilities in the source domain as evaluated by the teacher model. It learns to handle the speaker and environment variability inherent in and restricted to the speech signal in the target domain without proactively addressing the robustness to other likely conditions. Performance degradation may thus ensue.

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Authors:
Zhong Meng, Jinyu Li, Yifan Gong, Biing-Hwang (Fred) Juang
Submitted On:
12 May 2019 - 9:31pm
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[1] Zhong Meng, Jinyu Li, Yifan Gong, Biing-Hwang (Fred) Juang, "Adversarial Teacher-Student Learning for Unsupervised Adaptation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2506. Accessed: Oct. 16, 2019.
@article{2506-18,
url = {http://sigport.org/2506},
author = {Zhong Meng; Jinyu Li; Yifan Gong; Biing-Hwang (Fred) Juang },
publisher = {IEEE SigPort},
title = {Adversarial Teacher-Student Learning for Unsupervised Adaptation},
year = {2018} }
TY - EJOUR
T1 - Adversarial Teacher-Student Learning for Unsupervised Adaptation
AU - Zhong Meng; Jinyu Li; Yifan Gong; Biing-Hwang (Fred) Juang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2506
ER -
Zhong Meng, Jinyu Li, Yifan Gong, Biing-Hwang (Fred) Juang. (2018). Adversarial Teacher-Student Learning for Unsupervised Adaptation. IEEE SigPort. http://sigport.org/2506
Zhong Meng, Jinyu Li, Yifan Gong, Biing-Hwang (Fred) Juang, 2018. Adversarial Teacher-Student Learning for Unsupervised Adaptation. Available at: http://sigport.org/2506.
Zhong Meng, Jinyu Li, Yifan Gong, Biing-Hwang (Fred) Juang. (2018). "Adversarial Teacher-Student Learning for Unsupervised Adaptation." Web.
1. Zhong Meng, Jinyu Li, Yifan Gong, Biing-Hwang (Fred) Juang. Adversarial Teacher-Student Learning for Unsupervised Adaptation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2506

A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation


A domain adaptation method for urban scene segmentation is proposed in this work. We develop a fully convolutional tri-branch network, where two branches assign pseudo labels to images in the unlabeled target domain while the third branch is trained with supervision based on images in the pseudo-labeled target domain. The re-labeling and re-training processes alternate. With this design, the tri-branch network learns target-specific discriminative representations progressively and, as a result, the cross-domain capability of the segmenter improves.

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Authors:
Junting Zhang, Chen Liang, C.-C. Jay Kuo
Submitted On:
12 April 2018 - 5:32pm
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[1] Junting Zhang, Chen Liang, C.-C. Jay Kuo, "A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2503. Accessed: Oct. 16, 2019.
@article{2503-18,
url = {http://sigport.org/2503},
author = {Junting Zhang; Chen Liang; C.-C. Jay Kuo },
publisher = {IEEE SigPort},
title = {A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation},
year = {2018} }
TY - EJOUR
T1 - A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation
AU - Junting Zhang; Chen Liang; C.-C. Jay Kuo
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2503
ER -
Junting Zhang, Chen Liang, C.-C. Jay Kuo. (2018). A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation. IEEE SigPort. http://sigport.org/2503
Junting Zhang, Chen Liang, C.-C. Jay Kuo, 2018. A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation. Available at: http://sigport.org/2503.
Junting Zhang, Chen Liang, C.-C. Jay Kuo. (2018). "A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation." Web.
1. Junting Zhang, Chen Liang, C.-C. Jay Kuo. A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2503

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