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A Survey of Deep Active Learning

A Survey of Deep Active Learning

30 August 2020
Pengzhen Ren
Yun Xiao
Xiaojun Chang
Po-Yao (Bernie) Huang
Zhihui Li
Brij B. Gupta
Xiaojiang Chen
Xin Wang
ArXivPDFHTML

Papers citing "A Survey of Deep Active Learning"

20 / 120 papers shown
Title
Active Learning for Event Extraction with Memory-based Loss Prediction
  Model
Active Learning for Event Extraction with Memory-based Loss Prediction Model
Shirong Shen
Zhen Li
Guilin Qi
18
1
0
26 Nov 2021
Smart Data Representations: Impact on the Accuracy of Deep Neural
  Networks
Smart Data Representations: Impact on the Accuracy of Deep Neural Networks
Oliver Neumann
Nicole Ludwig
Marian Turowski
Benedikt Heidrich
V. Hagenmeyer
Ralf Mikut
AI4TS
39
1
0
17 Nov 2021
Diversity Enhanced Active Learning with Strictly Proper Scoring Rules
Diversity Enhanced Active Learning with Strictly Proper Scoring Rules
Wei Tan
Lan Du
Wray L. Buntine
16
30
0
27 Oct 2021
A Survey on Machine Learning Techniques for Source Code Analysis
A Survey on Machine Learning Techniques for Source Code Analysis
Tushar Sharma
M. Kechagia
Stefanos Georgiou
Rohit Tiwari
Indira Vats
Hadi Moazen
Federica Sarro
25
61
0
18 Oct 2021
ActiveEA: Active Learning for Neural Entity Alignment
ActiveEA: Active Learning for Neural Entity Alignment
Bing Liu
Harrisen Scells
Guido Zuccon
Wenlan Hua
Genghong Zhao
29
24
0
13 Oct 2021
Robust Contrastive Active Learning with Feature-guided Query Strategies
Robust Contrastive Active Learning with Feature-guided Query Strategies
R. Krishnan
Nilesh A. Ahuja
Alok Sinha
Mahesh Subedar
Omesh Tickoo
Ravi Iyer
18
1
0
13 Sep 2021
ImitAL: Learning Active Learning Strategies from Synthetic Data
ImitAL: Learning Active Learning Strategies from Synthetic Data
Julius Gonsior
Maik Thiele
Wolfgang Lehner
13
4
0
17 Aug 2021
Robust and Active Learning for Deep Neural Network Regression
Robust and Active Learning for Deep Neural Network Regression
Xi Li
G. Kesidis
David J. Miller
Maxime Bergeron
Ryan Ferguson
V. Lucic
20
1
0
28 Jul 2021
Algorithmic Bias and Data Bias: Understanding the Relation between
  Distributionally Robust Optimization and Data Curation
Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation
Agnieszka Słowik
Léon Bottou
FaML
43
19
0
17 Jun 2021
Active Learning for Network Traffic Classification: A Technical Study
Active Learning for Network Traffic Classification: A Technical Study
A. Shahraki
Mahmoud Abbasi
Amirhosein Taherkordi
A. Jurcut
19
42
0
13 Jun 2021
All you need are a few pixels: semantic segmentation with PixelPick
All you need are a few pixels: semantic segmentation with PixelPick
Gyungin Shin
Weidi Xie
Samuel Albanie
VLM
21
42
0
13 Apr 2021
Deep Indexed Active Learning for Matching Heterogeneous Entity
  Representations
Deep Indexed Active Learning for Matching Heterogeneous Entity Representations
Arjit Jain
Sunita Sarawagi
Prithviraj Sen
22
24
0
08 Apr 2021
Low-Regret Active learning
Low-Regret Active learning
Cenk Baykal
Lucas Liebenwein
Dan Feldman
Daniela Rus
UQCV
26
3
0
06 Apr 2021
Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report
  Generation
Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report Generation
Mingjie Li
Fuyu Wang
Xiaojun Chang
Xiaodan Liang
MedIm
21
101
0
06 Jun 2020
Bag of Tricks for Image Classification with Convolutional Neural
  Networks
Bag of Tricks for Image Classification with Convolutional Neural Networks
Tong He
Zhi-Li Zhang
Hang Zhang
Zhongyue Zhang
Junyuan Xie
Mu Li
221
1,399
0
04 Dec 2018
Neural Architecture Search with Reinforcement Learning
Neural Architecture Search with Reinforcement Learning
Barret Zoph
Quoc V. Le
271
5,326
0
05 Nov 2016
Conditional Image Synthesis With Auxiliary Classifier GANs
Conditional Image Synthesis With Auxiliary Classifier GANs
Augustus Odena
C. Olah
Jonathon Shlens
GAN
238
3,190
0
30 Oct 2016
Bayesian Convolutional Neural Networks with Bernoulli Approximate
  Variational Inference
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
Y. Gal
Zoubin Ghahramani
UQCV
BDL
197
745
0
06 Jun 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,136
0
06 Jun 2015
Improving neural networks by preventing co-adaptation of feature
  detectors
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton
Nitish Srivastava
A. Krizhevsky
Ilya Sutskever
Ruslan Salakhutdinov
VLM
266
7,634
0
03 Jul 2012
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