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Rethinking deep active learning: Using unlabeled data at model training

Rethinking deep active learning: Using unlabeled data at model training

19 November 2019
Oriane Siméoni
Mateusz Budnik
Yannis Avrithis
G. Gravier
    HAI
ArXivPDFHTML

Papers citing "Rethinking deep active learning: Using unlabeled data at model training"

16 / 16 papers shown
Title
Efficient Data Selection for Training Genomic Perturbation Models
Efficient Data Selection for Training Genomic Perturbation Models
G. Panagopoulos
J. Lutzeyer
Sofiane Ennadir
Michalis Vazirgiannis
Jun Pang
150
0
0
18 Mar 2025
CALICO: Confident Active Learning with Integrated Calibration
CALICO: Confident Active Learning with Integrated Calibration
L. S. Querol
Hajime Nagahara
Hideaki Hayashi
25
0
0
02 Jul 2024
Self-Training for Sample-Efficient Active Learning for Text
  Classification with Pre-Trained Language Models
Self-Training for Sample-Efficient Active Learning for Text Classification with Pre-Trained Language Models
Christopher Schröder
Gerhard Heyer
VLM
44
0
0
13 Jun 2024
Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active
  Learning
Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning
A. Hekimoglu
Michael Schmidt
Alvaro Marcos-Ramiro
3DPC
21
10
0
17 Jul 2023
Training Ensembles with Inliers and Outliers for Semi-supervised Active
  Learning
Training Ensembles with Inliers and Outliers for Semi-supervised Active Learning
Vladan Stojnić
Zakaria Laskar
Giorgos Tolias
31
0
0
07 Jul 2023
LabelBench: A Comprehensive Framework for Benchmarking Adaptive
  Label-Efficient Learning
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning
Jifan Zhang
Yifang Chen
Gregory H. Canal
Stephen Mussmann
Arnav M. Das
...
Yinglun Zhu
Jeffrey Bilmes
S. Du
Kevin G. Jamieson
Robert D. Nowak
VLM
33
10
0
16 Jun 2023
Improving Semi-supervised Deep Learning by using Automatic Thresholding
  to Deal with Out of Distribution Data for COVID-19 Detection using Chest
  X-ray Images
Improving Semi-supervised Deep Learning by using Automatic Thresholding to Deal with Out of Distribution Data for COVID-19 Detection using Chest X-ray Images
Isaac Benavides-Mata
Saul Calderon-Ramirez
16
3
0
03 Nov 2022
cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule
  Diagnosis
cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule Diagnosis
Jiahao Lu
Chong Yin
Kenny Erleben
M. B. Nielsen
S. Darkner
22
1
0
28 Oct 2022
Warm Start Active Learning with Proxy Labels \& Selection via
  Semi-Supervised Fine-Tuning
Warm Start Active Learning with Proxy Labels \& Selection via Semi-Supervised Fine-Tuning
V. Nath
Dong Yang
H. Roth
Daguang Xu
38
25
0
13 Sep 2022
Annotation Cost Reduction of Stream-based Active Learning by Automated
  Weak Labeling using a Robot Arm
Annotation Cost Reduction of Stream-based Active Learning by Automated Weak Labeling using a Robot Arm
Kanata Suzuki
Taro Sunagawa
Tomotake Sasaki
Takashi Katoh
13
3
0
03 Oct 2021
Multi-Domain Active Learning: Literature Review and Comparative Study
Multi-Domain Active Learning: Literature Review and Comparative Study
Ruidan He
Shengcai Liu
Shan He
Ke Tang
OOD
19
14
0
25 Jun 2021
Not All Labels Are Equal: Rationalizing The Labeling Costs for Training
  Object Detection
Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection
Ismail Elezi
Zhiding Yu
Anima Anandkumar
Laura Leal-Taixe
J. Álvarez
ObjD
18
39
0
22 Jun 2021
Low Budget Active Learning via Wasserstein Distance: An Integer
  Programming Approach
Low Budget Active Learning via Wasserstein Distance: An Integer Programming Approach
Rafid Mahmood
Sanja Fidler
M. Law
14
37
0
05 Jun 2021
On Initial Pools for Deep Active Learning
On Initial Pools for Deep Active Learning
Akshay L Chandra
Sai Vikas Desai
Chaitanya Devaguptapu
V. Balasubramanian
21
19
0
30 Nov 2020
Adversarial Representation Active Learning
Adversarial Representation Active Learning
A. Mottaghi
Serena Yeung
VLM
GAN
17
29
0
20 Dec 2019
Parting with Illusions about Deep Active Learning
Parting with Illusions about Deep Active Learning
Sudhanshu Mittal
Maxim Tatarchenko
Özgün Çiçek
Thomas Brox
VLM
19
59
0
11 Dec 2019
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