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ICASSP 2019

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The 2019 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit website

HIGH-QUALITY SPEECH CODING WITH SAMPLE RNN


We provide a speech coding scheme employing a generative model based on SampleRNN that, while operating at significantly lower bitrates, matches or surpasses the perceptual quality of state-of-the-art classic wide-band codecs. Moreover, it is demonstrated that the proposed scheme can provide a meaningful rate-distortion trade-off without retraining. We evaluate the proposed scheme in a series of listening tests and discuss limitations of the approach.

Paper Details

Authors:
Janusz Klejsa, Per Hedelin, Cong Zhou, Roy Fejgin, Lars Villemoes
Submitted On:
23 May 2019 - 7:33am
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[1] Janusz Klejsa, Per Hedelin, Cong Zhou, Roy Fejgin, Lars Villemoes, "HIGH-QUALITY SPEECH CODING WITH SAMPLE RNN", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3895. Accessed: Apr. 04, 2020.
@article{3895-19,
url = {http://sigport.org/3895},
author = {Janusz Klejsa; Per Hedelin; Cong Zhou; Roy Fejgin; Lars Villemoes },
publisher = {IEEE SigPort},
title = {HIGH-QUALITY SPEECH CODING WITH SAMPLE RNN},
year = {2019} }
TY - EJOUR
T1 - HIGH-QUALITY SPEECH CODING WITH SAMPLE RNN
AU - Janusz Klejsa; Per Hedelin; Cong Zhou; Roy Fejgin; Lars Villemoes
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3895
ER -
Janusz Klejsa, Per Hedelin, Cong Zhou, Roy Fejgin, Lars Villemoes. (2019). HIGH-QUALITY SPEECH CODING WITH SAMPLE RNN. IEEE SigPort. http://sigport.org/3895
Janusz Klejsa, Per Hedelin, Cong Zhou, Roy Fejgin, Lars Villemoes, 2019. HIGH-QUALITY SPEECH CODING WITH SAMPLE RNN. Available at: http://sigport.org/3895.
Janusz Klejsa, Per Hedelin, Cong Zhou, Roy Fejgin, Lars Villemoes. (2019). "HIGH-QUALITY SPEECH CODING WITH SAMPLE RNN." Web.
1. Janusz Klejsa, Per Hedelin, Cong Zhou, Roy Fejgin, Lars Villemoes. HIGH-QUALITY SPEECH CODING WITH SAMPLE RNN [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3895

LINEAR PREDICTION-BASED PART-DEFINED AUTO-ENCODER USED FOR SPEECH ENHANCEMENT


This paper proposes a linear prediction-based part-defined auto-encoder (PAE) network to enhance speech signal. The PAE is a defined decoder or an established encoder network, based on an efficient learning algorithm or classical model. In this paper, the PAE utilizes AR-Wiener filter as the decoder part, and the AR-Wiener filter is modified as a linear prediction (LP) model by incorporating the modified factor from the residual signal. The parameters of line spectral frequency (LSF) of speech and noise and the Wiener filtering mask are utilized for training targets.

Paper Details

Authors:
Zihao Cui; Changchun Bao
Submitted On:
16 April 2019 - 5:33am
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Poster_ICASSP_2019_LPPAE_zihao.pdf

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[1] Zihao Cui; Changchun Bao, "LINEAR PREDICTION-BASED PART-DEFINED AUTO-ENCODER USED FOR SPEECH ENHANCEMENT", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3894. Accessed: Apr. 04, 2020.
@article{3894-19,
url = {http://sigport.org/3894},
author = {Zihao Cui; Changchun Bao },
publisher = {IEEE SigPort},
title = {LINEAR PREDICTION-BASED PART-DEFINED AUTO-ENCODER USED FOR SPEECH ENHANCEMENT},
year = {2019} }
TY - EJOUR
T1 - LINEAR PREDICTION-BASED PART-DEFINED AUTO-ENCODER USED FOR SPEECH ENHANCEMENT
AU - Zihao Cui; Changchun Bao
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3894
ER -
Zihao Cui; Changchun Bao. (2019). LINEAR PREDICTION-BASED PART-DEFINED AUTO-ENCODER USED FOR SPEECH ENHANCEMENT. IEEE SigPort. http://sigport.org/3894
Zihao Cui; Changchun Bao, 2019. LINEAR PREDICTION-BASED PART-DEFINED AUTO-ENCODER USED FOR SPEECH ENHANCEMENT. Available at: http://sigport.org/3894.
Zihao Cui; Changchun Bao. (2019). "LINEAR PREDICTION-BASED PART-DEFINED AUTO-ENCODER USED FOR SPEECH ENHANCEMENT." Web.
1. Zihao Cui; Changchun Bao. LINEAR PREDICTION-BASED PART-DEFINED AUTO-ENCODER USED FOR SPEECH ENHANCEMENT [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3894

Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting


With the boosting requirements of realistic 3D modeling for immersive applications, advent of the newly-developed 3D point cloud has attracted great attention. Frankly, immersive experience using high data volume affirms the importance of efficient compression. Inspired by the video-based point cloud compression (V-PCC), we propose a novel point cloud compression algorithm based on polynomial fitting of proper patches. Moreover, the original point cloud is segmented into various patches.

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Authors:
Yingzhan Xu, Wenjie Zhu, Yiling Xu, Zhu Li
Submitted On:
15 April 2019 - 5:18am
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polynomial poster_24x48.pdf

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[1] Yingzhan Xu, Wenjie Zhu, Yiling Xu, Zhu Li, "Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3892. Accessed: Apr. 04, 2020.
@article{3892-19,
url = {http://sigport.org/3892},
author = {Yingzhan Xu; Wenjie Zhu; Yiling Xu; Zhu Li },
publisher = {IEEE SigPort},
title = {Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting},
year = {2019} }
TY - EJOUR
T1 - Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting
AU - Yingzhan Xu; Wenjie Zhu; Yiling Xu; Zhu Li
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3892
ER -
Yingzhan Xu, Wenjie Zhu, Yiling Xu, Zhu Li. (2019). Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting. IEEE SigPort. http://sigport.org/3892
Yingzhan Xu, Wenjie Zhu, Yiling Xu, Zhu Li, 2019. Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting. Available at: http://sigport.org/3892.
Yingzhan Xu, Wenjie Zhu, Yiling Xu, Zhu Li. (2019). "Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting." Web.
1. Yingzhan Xu, Wenjie Zhu, Yiling Xu, Zhu Li. Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3892

Crime event embedding with unsupervised feature selection


We present a novel event embedding algorithm for crime data that can jointly capture time, location, and the complex free-text component of each event. The embedding is achieved by regularized Restricted Boltzmann Machines (RBMs), and we introduce a new way to regularize by imposing a ℓ1 penalty on the conditional distributions of the observed variables of RBMs. This choice of regularization performs feature selection and it also leads to efficient computation since the gradient can be computed in a closed form.

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Authors:
Shixiang Zhu, Yao Xie
Submitted On:
14 April 2019 - 4:12pm
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Poster of the paper "Crime event embedding with unsupervised feature selection"

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[1] Shixiang Zhu, Yao Xie, "Crime event embedding with unsupervised feature selection", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3891. Accessed: Apr. 04, 2020.
@article{3891-19,
url = {http://sigport.org/3891},
author = {Shixiang Zhu; Yao Xie },
publisher = {IEEE SigPort},
title = {Crime event embedding with unsupervised feature selection},
year = {2019} }
TY - EJOUR
T1 - Crime event embedding with unsupervised feature selection
AU - Shixiang Zhu; Yao Xie
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3891
ER -
Shixiang Zhu, Yao Xie. (2019). Crime event embedding with unsupervised feature selection. IEEE SigPort. http://sigport.org/3891
Shixiang Zhu, Yao Xie, 2019. Crime event embedding with unsupervised feature selection. Available at: http://sigport.org/3891.
Shixiang Zhu, Yao Xie. (2019). "Crime event embedding with unsupervised feature selection." Web.
1. Shixiang Zhu, Yao Xie. Crime event embedding with unsupervised feature selection [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3891

Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS'

Paper Details

Authors:
Lijun Zhang, Wenge Rong, Baiwen Li, Qi Li
Submitted On:
13 April 2019 - 1:34am
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Poster-DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS.pdf

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[1] Lijun Zhang, Wenge Rong, Baiwen Li, Qi Li, "Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS'", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3890. Accessed: Apr. 04, 2020.
@article{3890-19,
url = {http://sigport.org/3890},
author = {Lijun Zhang; Wenge Rong; Baiwen Li; Qi Li },
publisher = {IEEE SigPort},
title = {Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS'},
year = {2019} }
TY - EJOUR
T1 - Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS'
AU - Lijun Zhang; Wenge Rong; Baiwen Li; Qi Li
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3890
ER -
Lijun Zhang, Wenge Rong, Baiwen Li, Qi Li. (2019). Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS'. IEEE SigPort. http://sigport.org/3890
Lijun Zhang, Wenge Rong, Baiwen Li, Qi Li, 2019. Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS'. Available at: http://sigport.org/3890.
Lijun Zhang, Wenge Rong, Baiwen Li, Qi Li. (2019). "Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS'." Web.
1. Lijun Zhang, Wenge Rong, Baiwen Li, Qi Li. Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS' [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3890

GRAPH-BASED RGB-D IMAGE SEGMENTATION USING COLOR-DIRECTIONAL-REGION MERGING


Color and depth information provided simultaneously in RGB-D images can be used to segment scenes into disjoint regions. In this paper, a graph-based segmentation method for RGB-D image is proposed, in which an adaptive data-driven combination of color- and normal-variation is presented to construct dissimilarity between two adjacent pixels and a novel region merging threshold exploiting normal information in adjacent regions is proposed to control the proceeding of the region merging.

Paper Details

Authors:
Xiong Pan, Zejun Zhang, Yizhang Liu, Changcai Yang, Qiufeng Chen, Li Cheng, Jiaxiang Lin, Riqing Chen
Submitted On:
18 April 2019 - 11:15pm
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GRAPH-BASED RGB-D IMAGE SEGMENTATION USING COLOR-DIRECTIONAL-REGION MERGING.pdf

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[1] Xiong Pan, Zejun Zhang, Yizhang Liu, Changcai Yang, Qiufeng Chen, Li Cheng, Jiaxiang Lin, Riqing Chen, "GRAPH-BASED RGB-D IMAGE SEGMENTATION USING COLOR-DIRECTIONAL-REGION MERGING", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3889. Accessed: Apr. 04, 2020.
@article{3889-19,
url = {http://sigport.org/3889},
author = {Xiong Pan; Zejun Zhang; Yizhang Liu; Changcai Yang; Qiufeng Chen; Li Cheng; Jiaxiang Lin; Riqing Chen },
publisher = {IEEE SigPort},
title = {GRAPH-BASED RGB-D IMAGE SEGMENTATION USING COLOR-DIRECTIONAL-REGION MERGING},
year = {2019} }
TY - EJOUR
T1 - GRAPH-BASED RGB-D IMAGE SEGMENTATION USING COLOR-DIRECTIONAL-REGION MERGING
AU - Xiong Pan; Zejun Zhang; Yizhang Liu; Changcai Yang; Qiufeng Chen; Li Cheng; Jiaxiang Lin; Riqing Chen
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3889
ER -
Xiong Pan, Zejun Zhang, Yizhang Liu, Changcai Yang, Qiufeng Chen, Li Cheng, Jiaxiang Lin, Riqing Chen. (2019). GRAPH-BASED RGB-D IMAGE SEGMENTATION USING COLOR-DIRECTIONAL-REGION MERGING. IEEE SigPort. http://sigport.org/3889
Xiong Pan, Zejun Zhang, Yizhang Liu, Changcai Yang, Qiufeng Chen, Li Cheng, Jiaxiang Lin, Riqing Chen, 2019. GRAPH-BASED RGB-D IMAGE SEGMENTATION USING COLOR-DIRECTIONAL-REGION MERGING. Available at: http://sigport.org/3889.
Xiong Pan, Zejun Zhang, Yizhang Liu, Changcai Yang, Qiufeng Chen, Li Cheng, Jiaxiang Lin, Riqing Chen. (2019). "GRAPH-BASED RGB-D IMAGE SEGMENTATION USING COLOR-DIRECTIONAL-REGION MERGING." Web.
1. Xiong Pan, Zejun Zhang, Yizhang Liu, Changcai Yang, Qiufeng Chen, Li Cheng, Jiaxiang Lin, Riqing Chen. GRAPH-BASED RGB-D IMAGE SEGMENTATION USING COLOR-DIRECTIONAL-REGION MERGING [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3889

USING DEEP-Q NETWORK TO SELECT CANDIDATES FROM N-BEST SPEECH RECOGNITION HYPOTHESES FOR ENHANCING DIALOGUE STATE TRACKING

Paper Details

Authors:
Richard Tzong-Han Tsai, Chia-Hao Chen, Chun-Kai Wu, Yu-Cheng Hsiao, Hung-Yi Lee
Submitted On:
11 April 2019 - 4:05am
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ICASSP 2019#2338.pdf

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[1] Richard Tzong-Han Tsai, Chia-Hao Chen, Chun-Kai Wu, Yu-Cheng Hsiao, Hung-Yi Lee, "USING DEEP-Q NETWORK TO SELECT CANDIDATES FROM N-BEST SPEECH RECOGNITION HYPOTHESES FOR ENHANCING DIALOGUE STATE TRACKING", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3888. Accessed: Apr. 04, 2020.
@article{3888-19,
url = {http://sigport.org/3888},
author = {Richard Tzong-Han Tsai; Chia-Hao Chen; Chun-Kai Wu; Yu-Cheng Hsiao; Hung-Yi Lee },
publisher = {IEEE SigPort},
title = {USING DEEP-Q NETWORK TO SELECT CANDIDATES FROM N-BEST SPEECH RECOGNITION HYPOTHESES FOR ENHANCING DIALOGUE STATE TRACKING},
year = {2019} }
TY - EJOUR
T1 - USING DEEP-Q NETWORK TO SELECT CANDIDATES FROM N-BEST SPEECH RECOGNITION HYPOTHESES FOR ENHANCING DIALOGUE STATE TRACKING
AU - Richard Tzong-Han Tsai; Chia-Hao Chen; Chun-Kai Wu; Yu-Cheng Hsiao; Hung-Yi Lee
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3888
ER -
Richard Tzong-Han Tsai, Chia-Hao Chen, Chun-Kai Wu, Yu-Cheng Hsiao, Hung-Yi Lee. (2019). USING DEEP-Q NETWORK TO SELECT CANDIDATES FROM N-BEST SPEECH RECOGNITION HYPOTHESES FOR ENHANCING DIALOGUE STATE TRACKING. IEEE SigPort. http://sigport.org/3888
Richard Tzong-Han Tsai, Chia-Hao Chen, Chun-Kai Wu, Yu-Cheng Hsiao, Hung-Yi Lee, 2019. USING DEEP-Q NETWORK TO SELECT CANDIDATES FROM N-BEST SPEECH RECOGNITION HYPOTHESES FOR ENHANCING DIALOGUE STATE TRACKING. Available at: http://sigport.org/3888.
Richard Tzong-Han Tsai, Chia-Hao Chen, Chun-Kai Wu, Yu-Cheng Hsiao, Hung-Yi Lee. (2019). "USING DEEP-Q NETWORK TO SELECT CANDIDATES FROM N-BEST SPEECH RECOGNITION HYPOTHESES FOR ENHANCING DIALOGUE STATE TRACKING." Web.
1. Richard Tzong-Han Tsai, Chia-Hao Chen, Chun-Kai Wu, Yu-Cheng Hsiao, Hung-Yi Lee. USING DEEP-Q NETWORK TO SELECT CANDIDATES FROM N-BEST SPEECH RECOGNITION HYPOTHESES FOR ENHANCING DIALOGUE STATE TRACKING [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3888

BEYOND WORD-LEVEL TO SENTENCE-LEVEL SENTIMENT ANALYSIS FOR FINANCIAL REPORTS

Paper Details

Authors:
Chi-Han Du, Ming-Feng Tsai, Chuan-Ju Wang
Submitted On:
27 March 2019 - 9:06am
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ICASSP19_1439.pdf

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[1] Chi-Han Du, Ming-Feng Tsai, Chuan-Ju Wang, "BEYOND WORD-LEVEL TO SENTENCE-LEVEL SENTIMENT ANALYSIS FOR FINANCIAL REPORTS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3857. Accessed: Apr. 04, 2020.
@article{3857-19,
url = {http://sigport.org/3857},
author = {Chi-Han Du; Ming-Feng Tsai; Chuan-Ju Wang },
publisher = {IEEE SigPort},
title = {BEYOND WORD-LEVEL TO SENTENCE-LEVEL SENTIMENT ANALYSIS FOR FINANCIAL REPORTS},
year = {2019} }
TY - EJOUR
T1 - BEYOND WORD-LEVEL TO SENTENCE-LEVEL SENTIMENT ANALYSIS FOR FINANCIAL REPORTS
AU - Chi-Han Du; Ming-Feng Tsai; Chuan-Ju Wang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3857
ER -
Chi-Han Du, Ming-Feng Tsai, Chuan-Ju Wang. (2019). BEYOND WORD-LEVEL TO SENTENCE-LEVEL SENTIMENT ANALYSIS FOR FINANCIAL REPORTS. IEEE SigPort. http://sigport.org/3857
Chi-Han Du, Ming-Feng Tsai, Chuan-Ju Wang, 2019. BEYOND WORD-LEVEL TO SENTENCE-LEVEL SENTIMENT ANALYSIS FOR FINANCIAL REPORTS. Available at: http://sigport.org/3857.
Chi-Han Du, Ming-Feng Tsai, Chuan-Ju Wang. (2019). "BEYOND WORD-LEVEL TO SENTENCE-LEVEL SENTIMENT ANALYSIS FOR FINANCIAL REPORTS." Web.
1. Chi-Han Du, Ming-Feng Tsai, Chuan-Ju Wang. BEYOND WORD-LEVEL TO SENTENCE-LEVEL SENTIMENT ANALYSIS FOR FINANCIAL REPORTS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3857

A VIDEO CAMERA MODEL IDENTIFICATION SYSTEM USING DEEP LEARNING AND FUSION


While significant work has been conducted to perform source cam- era model identification for images, little work has been done specif- ically for video camera model identification. This is problematic because different forensic traces may be left in digital images and videos captured by the same camera. As our experiments in this paper will show, a system trained to perform camera model identifi- cation for images yields unacceptably low performance when given video frames from the same cameras. To overcome this problem, new systems for identifying a videos source must be developed.

Paper Details

Authors:
B. Hosler, O. Mayer, B. Bayar, X. Zhao, C. Chen, J. A. Shackleford, M. C. Stamm
Submitted On:
27 March 2019 - 9:03am
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ICASSP2019.pdf

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[1] B. Hosler, O. Mayer, B. Bayar, X. Zhao, C. Chen, J. A. Shackleford, M. C. Stamm, "A VIDEO CAMERA MODEL IDENTIFICATION SYSTEM USING DEEP LEARNING AND FUSION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3856. Accessed: Apr. 04, 2020.
@article{3856-19,
url = {http://sigport.org/3856},
author = {B. Hosler; O. Mayer; B. Bayar; X. Zhao; C. Chen; J. A. Shackleford; M. C. Stamm },
publisher = {IEEE SigPort},
title = {A VIDEO CAMERA MODEL IDENTIFICATION SYSTEM USING DEEP LEARNING AND FUSION},
year = {2019} }
TY - EJOUR
T1 - A VIDEO CAMERA MODEL IDENTIFICATION SYSTEM USING DEEP LEARNING AND FUSION
AU - B. Hosler; O. Mayer; B. Bayar; X. Zhao; C. Chen; J. A. Shackleford; M. C. Stamm
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3856
ER -
B. Hosler, O. Mayer, B. Bayar, X. Zhao, C. Chen, J. A. Shackleford, M. C. Stamm. (2019). A VIDEO CAMERA MODEL IDENTIFICATION SYSTEM USING DEEP LEARNING AND FUSION. IEEE SigPort. http://sigport.org/3856
B. Hosler, O. Mayer, B. Bayar, X. Zhao, C. Chen, J. A. Shackleford, M. C. Stamm, 2019. A VIDEO CAMERA MODEL IDENTIFICATION SYSTEM USING DEEP LEARNING AND FUSION. Available at: http://sigport.org/3856.
B. Hosler, O. Mayer, B. Bayar, X. Zhao, C. Chen, J. A. Shackleford, M. C. Stamm. (2019). "A VIDEO CAMERA MODEL IDENTIFICATION SYSTEM USING DEEP LEARNING AND FUSION." Web.
1. B. Hosler, O. Mayer, B. Bayar, X. Zhao, C. Chen, J. A. Shackleford, M. C. Stamm. A VIDEO CAMERA MODEL IDENTIFICATION SYSTEM USING DEEP LEARNING AND FUSION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3856

ACTIVE LEARNING WITH LABEL PROPORTIONS


Active Learning (AL) refers to the setting where the learner has the ability to perform queries to an oracle to acquire the true label of an instance or, sometimes, a set of instances. Even though Active Learning has been studied extensively, the setting is usually restricted to assume that the oracle is trustworthy and will provide the actual label. We argue that, while common, this approach can be made more flexible to account for different forms of supervision.

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Authors:
Raul Santos-Rodriguez, Niall Twomey
Submitted On:
1 March 2019 - 1:22pm
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[1] Raul Santos-Rodriguez, Niall Twomey, "ACTIVE LEARNING WITH LABEL PROPORTIONS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3855. Accessed: Apr. 04, 2020.
@article{3855-19,
url = {http://sigport.org/3855},
author = {Raul Santos-Rodriguez; Niall Twomey },
publisher = {IEEE SigPort},
title = {ACTIVE LEARNING WITH LABEL PROPORTIONS},
year = {2019} }
TY - EJOUR
T1 - ACTIVE LEARNING WITH LABEL PROPORTIONS
AU - Raul Santos-Rodriguez; Niall Twomey
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3855
ER -
Raul Santos-Rodriguez, Niall Twomey. (2019). ACTIVE LEARNING WITH LABEL PROPORTIONS. IEEE SigPort. http://sigport.org/3855
Raul Santos-Rodriguez, Niall Twomey, 2019. ACTIVE LEARNING WITH LABEL PROPORTIONS. Available at: http://sigport.org/3855.
Raul Santos-Rodriguez, Niall Twomey. (2019). "ACTIVE LEARNING WITH LABEL PROPORTIONS." Web.
1. Raul Santos-Rodriguez, Niall Twomey. ACTIVE LEARNING WITH LABEL PROPORTIONS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3855

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