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ICASSP 2020

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The ICASSP 2020 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.

Recovery of binary sparse signals from compressed linear measurements via polynomial optimization

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20 May 2020 - 5:38am
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[1] , "Recovery of binary sparse signals from compressed linear measurements via polynomial optimization", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5405. Accessed: Oct. 27, 2020.
@article{5405-20,
url = {http://sigport.org/5405},
author = { },
publisher = {IEEE SigPort},
title = {Recovery of binary sparse signals from compressed linear measurements via polynomial optimization},
year = {2020} }
TY - EJOUR
T1 - Recovery of binary sparse signals from compressed linear measurements via polynomial optimization
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5405
ER -
. (2020). Recovery of binary sparse signals from compressed linear measurements via polynomial optimization. IEEE SigPort. http://sigport.org/5405
, 2020. Recovery of binary sparse signals from compressed linear measurements via polynomial optimization. Available at: http://sigport.org/5405.
. (2020). "Recovery of binary sparse signals from compressed linear measurements via polynomial optimization." Web.
1. . Recovery of binary sparse signals from compressed linear measurements via polynomial optimization [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5405

DEEP ENCODED LINGUISTIC AND ACOUSTIC CUES FOR ATTENTION BASED END TO END SPEECH EMOTION RECOGNITION

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Swapnil Bhosale, Rupayan Chakraborty, Sunil Kumar Kopparapu
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20 May 2020 - 5:22am
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[1] Swapnil Bhosale, Rupayan Chakraborty, Sunil Kumar Kopparapu, "DEEP ENCODED LINGUISTIC AND ACOUSTIC CUES FOR ATTENTION BASED END TO END SPEECH EMOTION RECOGNITION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5404. Accessed: Oct. 27, 2020.
@article{5404-20,
url = {http://sigport.org/5404},
author = {Swapnil Bhosale; Rupayan Chakraborty; Sunil Kumar Kopparapu },
publisher = {IEEE SigPort},
title = {DEEP ENCODED LINGUISTIC AND ACOUSTIC CUES FOR ATTENTION BASED END TO END SPEECH EMOTION RECOGNITION},
year = {2020} }
TY - EJOUR
T1 - DEEP ENCODED LINGUISTIC AND ACOUSTIC CUES FOR ATTENTION BASED END TO END SPEECH EMOTION RECOGNITION
AU - Swapnil Bhosale; Rupayan Chakraborty; Sunil Kumar Kopparapu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5404
ER -
Swapnil Bhosale, Rupayan Chakraborty, Sunil Kumar Kopparapu. (2020). DEEP ENCODED LINGUISTIC AND ACOUSTIC CUES FOR ATTENTION BASED END TO END SPEECH EMOTION RECOGNITION. IEEE SigPort. http://sigport.org/5404
Swapnil Bhosale, Rupayan Chakraborty, Sunil Kumar Kopparapu, 2020. DEEP ENCODED LINGUISTIC AND ACOUSTIC CUES FOR ATTENTION BASED END TO END SPEECH EMOTION RECOGNITION. Available at: http://sigport.org/5404.
Swapnil Bhosale, Rupayan Chakraborty, Sunil Kumar Kopparapu. (2020). "DEEP ENCODED LINGUISTIC AND ACOUSTIC CUES FOR ATTENTION BASED END TO END SPEECH EMOTION RECOGNITION." Web.
1. Swapnil Bhosale, Rupayan Chakraborty, Sunil Kumar Kopparapu. DEEP ENCODED LINGUISTIC AND ACOUSTIC CUES FOR ATTENTION BASED END TO END SPEECH EMOTION RECOGNITION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5404

Multi-Conditioning & Data Augmentation using Generative Noise Model for Speech Emotion Recognition in Noisy Conditions


Degradation due to additive noise is a significant road block in the real-life deployment of Speech Emotion Recognition (SER) systems. Most of the previous work in this field dealt with the noise degradation either at the signal or at the feature level. In this paper, to address the robustness aspect of the SER in additive noise scenarios, we propose multi-conditioning and data augmentation using an utterance level parametric generative noise model. The generative noise model is designed to generate noise types which can span the entire noise space in the mel-filterbank energy domain.

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Upasana Tiwari, Meet Soni, Rupayan Chakraborty, Ashish Panda, Sunil Kumar Kopparapu
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20 May 2020 - 5:01am
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ICASSP2020_ppt_5701.pdf

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[1] Upasana Tiwari, Meet Soni, Rupayan Chakraborty, Ashish Panda, Sunil Kumar Kopparapu, "Multi-Conditioning & Data Augmentation using Generative Noise Model for Speech Emotion Recognition in Noisy Conditions", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5403. Accessed: Oct. 27, 2020.
@article{5403-20,
url = {http://sigport.org/5403},
author = {Upasana Tiwari; Meet Soni; Rupayan Chakraborty; Ashish Panda; Sunil Kumar Kopparapu },
publisher = {IEEE SigPort},
title = {Multi-Conditioning & Data Augmentation using Generative Noise Model for Speech Emotion Recognition in Noisy Conditions},
year = {2020} }
TY - EJOUR
T1 - Multi-Conditioning & Data Augmentation using Generative Noise Model for Speech Emotion Recognition in Noisy Conditions
AU - Upasana Tiwari; Meet Soni; Rupayan Chakraborty; Ashish Panda; Sunil Kumar Kopparapu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5403
ER -
Upasana Tiwari, Meet Soni, Rupayan Chakraborty, Ashish Panda, Sunil Kumar Kopparapu. (2020). Multi-Conditioning & Data Augmentation using Generative Noise Model for Speech Emotion Recognition in Noisy Conditions. IEEE SigPort. http://sigport.org/5403
Upasana Tiwari, Meet Soni, Rupayan Chakraborty, Ashish Panda, Sunil Kumar Kopparapu, 2020. Multi-Conditioning & Data Augmentation using Generative Noise Model for Speech Emotion Recognition in Noisy Conditions. Available at: http://sigport.org/5403.
Upasana Tiwari, Meet Soni, Rupayan Chakraborty, Ashish Panda, Sunil Kumar Kopparapu. (2020). "Multi-Conditioning & Data Augmentation using Generative Noise Model for Speech Emotion Recognition in Noisy Conditions." Web.
1. Upasana Tiwari, Meet Soni, Rupayan Chakraborty, Ashish Panda, Sunil Kumar Kopparapu. Multi-Conditioning & Data Augmentation using Generative Noise Model for Speech Emotion Recognition in Noisy Conditions [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5403

Efficient Techniques for In-band System Information Broadcast in Multi-cell Massive MIMO

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Authors:
Jinu Jayachandran, Kamal Biswas, Saif Khan Mohammed, Erik G. Larsson
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20 May 2020 - 1:16am
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Efficient Techniques for In-band System Information Broadcast in Multi-cell Massive MIMO

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[1] Jinu Jayachandran, Kamal Biswas, Saif Khan Mohammed, Erik G. Larsson, "Efficient Techniques for In-band System Information Broadcast in Multi-cell Massive MIMO", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5402. Accessed: Oct. 27, 2020.
@article{5402-20,
url = {http://sigport.org/5402},
author = {Jinu Jayachandran; Kamal Biswas; Saif Khan Mohammed; Erik G. Larsson },
publisher = {IEEE SigPort},
title = {Efficient Techniques for In-band System Information Broadcast in Multi-cell Massive MIMO},
year = {2020} }
TY - EJOUR
T1 - Efficient Techniques for In-band System Information Broadcast in Multi-cell Massive MIMO
AU - Jinu Jayachandran; Kamal Biswas; Saif Khan Mohammed; Erik G. Larsson
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5402
ER -
Jinu Jayachandran, Kamal Biswas, Saif Khan Mohammed, Erik G. Larsson. (2020). Efficient Techniques for In-band System Information Broadcast in Multi-cell Massive MIMO. IEEE SigPort. http://sigport.org/5402
Jinu Jayachandran, Kamal Biswas, Saif Khan Mohammed, Erik G. Larsson, 2020. Efficient Techniques for In-band System Information Broadcast in Multi-cell Massive MIMO. Available at: http://sigport.org/5402.
Jinu Jayachandran, Kamal Biswas, Saif Khan Mohammed, Erik G. Larsson. (2020). "Efficient Techniques for In-band System Information Broadcast in Multi-cell Massive MIMO." Web.
1. Jinu Jayachandran, Kamal Biswas, Saif Khan Mohammed, Erik G. Larsson. Efficient Techniques for In-band System Information Broadcast in Multi-cell Massive MIMO [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5402

FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR


Bridge weigh-in-motion (BWIM) is a technique for detecting heavy vehicles that may cause serious damage to real bridges. BWIM is realized by analyzing the strain signals observed at places on the bridge in terms of bridge-component responses to the axle loads. In current practice, a BWIM system requires multiple strain sensors to collect vehicle properties including speed and axle positions for accurate load estimation, which may limit the system’s life-span.

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Authors:
Takaya Kawakatsu, Kenro Aihara, Atsuhiro Takasu, Jun Adachi
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27 May 2020 - 11:14am
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icassp20s.pdf

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[1] Takaya Kawakatsu, Kenro Aihara, Atsuhiro Takasu, Jun Adachi, "FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5401. Accessed: Oct. 27, 2020.
@article{5401-20,
url = {http://sigport.org/5401},
author = {Takaya Kawakatsu; Kenro Aihara; Atsuhiro Takasu; Jun Adachi },
publisher = {IEEE SigPort},
title = {FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR},
year = {2020} }
TY - EJOUR
T1 - FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR
AU - Takaya Kawakatsu; Kenro Aihara; Atsuhiro Takasu; Jun Adachi
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5401
ER -
Takaya Kawakatsu, Kenro Aihara, Atsuhiro Takasu, Jun Adachi. (2020). FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR. IEEE SigPort. http://sigport.org/5401
Takaya Kawakatsu, Kenro Aihara, Atsuhiro Takasu, Jun Adachi, 2020. FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR. Available at: http://sigport.org/5401.
Takaya Kawakatsu, Kenro Aihara, Atsuhiro Takasu, Jun Adachi. (2020). "FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR." Web.
1. Takaya Kawakatsu, Kenro Aihara, Atsuhiro Takasu, Jun Adachi. FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5401

Conditional Density Driven Grid Design in Point-Mass Filter

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19 May 2020 - 8:27am
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[1] , "Conditional Density Driven Grid Design in Point-Mass Filter", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5400. Accessed: Oct. 27, 2020.
@article{5400-20,
url = {http://sigport.org/5400},
author = { },
publisher = {IEEE SigPort},
title = {Conditional Density Driven Grid Design in Point-Mass Filter},
year = {2020} }
TY - EJOUR
T1 - Conditional Density Driven Grid Design in Point-Mass Filter
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5400
ER -
. (2020). Conditional Density Driven Grid Design in Point-Mass Filter. IEEE SigPort. http://sigport.org/5400
, 2020. Conditional Density Driven Grid Design in Point-Mass Filter. Available at: http://sigport.org/5400.
. (2020). "Conditional Density Driven Grid Design in Point-Mass Filter." Web.
1. . Conditional Density Driven Grid Design in Point-Mass Filter [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5400

A REAL-TIME DEEP NETWORK FOR CROWD COUNTING


Automatic analysis of highly crowded people has attracted extensive attention from computer vision research. Previous approaches for crowd counting have already achieved promising performance across various benchmarks. However, to deal with the real situation, we hope the model run as fast as possible while keeping accuracy. In this paper, we propose a compact convolutional neural network for crowd counting which learns a more efficient model with a small number of parameters.

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19 May 2020 - 6:14am
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[1] , "A REAL-TIME DEEP NETWORK FOR CROWD COUNTING", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5399. Accessed: Oct. 27, 2020.
@article{5399-20,
url = {http://sigport.org/5399},
author = { },
publisher = {IEEE SigPort},
title = {A REAL-TIME DEEP NETWORK FOR CROWD COUNTING},
year = {2020} }
TY - EJOUR
T1 - A REAL-TIME DEEP NETWORK FOR CROWD COUNTING
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5399
ER -
. (2020). A REAL-TIME DEEP NETWORK FOR CROWD COUNTING. IEEE SigPort. http://sigport.org/5399
, 2020. A REAL-TIME DEEP NETWORK FOR CROWD COUNTING. Available at: http://sigport.org/5399.
. (2020). "A REAL-TIME DEEP NETWORK FOR CROWD COUNTING." Web.
1. . A REAL-TIME DEEP NETWORK FOR CROWD COUNTING [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5399

View-angle Invariant Object Monitoring Without Image Registration


Object monitoring can be performed by change detection algorithms. However, for the image pair with a large perspective difference, the change detection performance is usually impacted by inaccurate image registration. To address the above difficulties, a novel object-specific change detection approach is proposed for object monitoring in this paper. In contrast to traditional approaches, the proposed approach is robust to view angle variation and does not require explicit image registration. Experiments demonstrate the effectiveness and advantages of the proposed approach.

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Xin Zhang; Chunlei Huo; Chunhong Pan
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19 May 2020 - 4:58am
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View-angle Invariant Object Monitoring Without Image Registration .pdf

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[1] Xin Zhang; Chunlei Huo; Chunhong Pan, "View-angle Invariant Object Monitoring Without Image Registration ", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5398. Accessed: Oct. 27, 2020.
@article{5398-20,
url = {http://sigport.org/5398},
author = {Xin Zhang; Chunlei Huo; Chunhong Pan },
publisher = {IEEE SigPort},
title = {View-angle Invariant Object Monitoring Without Image Registration },
year = {2020} }
TY - EJOUR
T1 - View-angle Invariant Object Monitoring Without Image Registration
AU - Xin Zhang; Chunlei Huo; Chunhong Pan
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5398
ER -
Xin Zhang; Chunlei Huo; Chunhong Pan. (2020). View-angle Invariant Object Monitoring Without Image Registration . IEEE SigPort. http://sigport.org/5398
Xin Zhang; Chunlei Huo; Chunhong Pan, 2020. View-angle Invariant Object Monitoring Without Image Registration . Available at: http://sigport.org/5398.
Xin Zhang; Chunlei Huo; Chunhong Pan. (2020). "View-angle Invariant Object Monitoring Without Image Registration ." Web.
1. Xin Zhang; Chunlei Huo; Chunhong Pan. View-angle Invariant Object Monitoring Without Image Registration [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5398

IMPROVING THE PERFORMANCE OF TRANSFORMER BASED LOW RESOURCE SPEECH RECOGNITION FOR INDIAN LANGUAGES


The recent success of the Transformer based sequence-to-sequence framework for various Natural Language Processing tasks has motivated its application to Automatic Speech Recognition. In this work, we explore the application of Transformers on low resource Indian languages in a multilingual framework. We explore various methods to incorporate language information into a multilingual Transformer, i.e.,(i) at the decoder, (ii) at the encoder. These methods include using language identity tokens or providing language information to the acoustic vectors.

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Authors:
Vishwas M. Shetty, Metilda Sagaya Mary N J, S. Umesh
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19 May 2020 - 3:23am
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[1] Vishwas M. Shetty, Metilda Sagaya Mary N J, S. Umesh, "IMPROVING THE PERFORMANCE OF TRANSFORMER BASED LOW RESOURCE SPEECH RECOGNITION FOR INDIAN LANGUAGES", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5397. Accessed: Oct. 27, 2020.
@article{5397-20,
url = {http://sigport.org/5397},
author = {Vishwas M. Shetty; Metilda Sagaya Mary N J; S. Umesh },
publisher = {IEEE SigPort},
title = {IMPROVING THE PERFORMANCE OF TRANSFORMER BASED LOW RESOURCE SPEECH RECOGNITION FOR INDIAN LANGUAGES},
year = {2020} }
TY - EJOUR
T1 - IMPROVING THE PERFORMANCE OF TRANSFORMER BASED LOW RESOURCE SPEECH RECOGNITION FOR INDIAN LANGUAGES
AU - Vishwas M. Shetty; Metilda Sagaya Mary N J; S. Umesh
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5397
ER -
Vishwas M. Shetty, Metilda Sagaya Mary N J, S. Umesh. (2020). IMPROVING THE PERFORMANCE OF TRANSFORMER BASED LOW RESOURCE SPEECH RECOGNITION FOR INDIAN LANGUAGES. IEEE SigPort. http://sigport.org/5397
Vishwas M. Shetty, Metilda Sagaya Mary N J, S. Umesh, 2020. IMPROVING THE PERFORMANCE OF TRANSFORMER BASED LOW RESOURCE SPEECH RECOGNITION FOR INDIAN LANGUAGES. Available at: http://sigport.org/5397.
Vishwas M. Shetty, Metilda Sagaya Mary N J, S. Umesh. (2020). "IMPROVING THE PERFORMANCE OF TRANSFORMER BASED LOW RESOURCE SPEECH RECOGNITION FOR INDIAN LANGUAGES." Web.
1. Vishwas M. Shetty, Metilda Sagaya Mary N J, S. Umesh. IMPROVING THE PERFORMANCE OF TRANSFORMER BASED LOW RESOURCE SPEECH RECOGNITION FOR INDIAN LANGUAGES [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5397

WITCHcraft: Efficient PGD Attacks with Random Step Size

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Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi
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18 May 2020 - 6:05pm
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WITCHcraft ICASSP 2020.pdf

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[1] Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi, "WITCHcraft: Efficient PGD Attacks with Random Step Size", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5396. Accessed: Oct. 27, 2020.
@article{5396-20,
url = {http://sigport.org/5396},
author = {Ping-Yeh Chiang; Jonas Geiping; Micah Goldblum; Tom Goldstein; Renkun Ni; Steven Reich; Ali Shafahi },
publisher = {IEEE SigPort},
title = {WITCHcraft: Efficient PGD Attacks with Random Step Size},
year = {2020} }
TY - EJOUR
T1 - WITCHcraft: Efficient PGD Attacks with Random Step Size
AU - Ping-Yeh Chiang; Jonas Geiping; Micah Goldblum; Tom Goldstein; Renkun Ni; Steven Reich; Ali Shafahi
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5396
ER -
Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi. (2020). WITCHcraft: Efficient PGD Attacks with Random Step Size. IEEE SigPort. http://sigport.org/5396
Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi, 2020. WITCHcraft: Efficient PGD Attacks with Random Step Size. Available at: http://sigport.org/5396.
Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi. (2020). "WITCHcraft: Efficient PGD Attacks with Random Step Size." Web.
1. Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi. WITCHcraft: Efficient PGD Attacks with Random Step Size [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5396

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