ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2205.11320
  4. Cited By
Active Learning Through a Covering Lens
v1v2v3 (latest)

Active Learning Through a Covering Lens

23 May 2022
Ofer Yehuda
Avihu Dekel
Guy Hacohen
D. Weinshall
ArXiv (abs)PDFHTMLGithub (93★)

Papers citing "Active Learning Through a Covering Lens"

38 / 38 papers shown
Title
Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training
Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training
Paul Doucet
Benjamin Estermann
Till Aczél
Roger Wattenhofer
216
4
0
20 Jan 2025
Uncertainty Herding: One Active Learning Method for All Label Budgets
Uncertainty Herding: One Active Learning Method for All Label Budgets
Wonho Bae
Gabriel L. Oliveira
Danica J. Sutherland
UQCV
422
0
0
30 Dec 2024
Deep Active Learning in the Open World
Deep Active Learning in the Open World
Tian Xie
Jifan Zhang
Haoyue Bai
R. Nowak
VLM
412
3
0
10 Nov 2024
Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model
Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model
Jaehyuk Heo
Pilsung Kang
VLM
74
1
0
09 Aug 2024
Making Your First Choice: To Address Cold Start Problem in Vision Active
  Learning
Making Your First Choice: To Address Cold Start Problem in Vision Active Learning
Liangyu Chen
Yutong Bai
Siyu Huang
Yongyi Lu
Bihan Wen
Alan Yuille
Zongwei Zhou
53
24
0
05 Oct 2022
Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A
  Prompt-Based Uncertainty Propagation Approach
Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approach
Yue Yu
Rongzhi Zhang
Ran Xu
Jieyu Zhang
Jiaming Shen
Chao Zhang
96
21
0
15 Sep 2022
Active Learning on a Budget: Opposite Strategies Suit High and Low
  Budgets
Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets
Guy Hacohen
Avihu Dekel
D. Weinshall
188
123
0
06 Feb 2022
A Simple Baseline for Low-Budget Active Learning
A Simple Baseline for Low-Budget Active Learning
Kossar Pourahmadi
Parsa Nooralinejad
Hamed Pirsiavash
80
20
0
22 Oct 2021
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo
  Labeling
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling
Bowen Zhang
Yidong Wang
Wenxin Hou
Hao Wu
Jindong Wang
Manabu Okumura
T. Shinozaki
AAML
344
896
0
15 Oct 2021
Reducing Label Effort: Self-Supervised meets Active Learning
Reducing Label Effort: Self-Supervised meets Active Learning
Javad Zolfaghari Bengar
Joost van de Weijer
Bartlomiej Twardowski
Bogdan Raducanu
VLM
78
60
0
25 Aug 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
86
37
0
05 Jun 2021
Emerging Properties in Self-Supervised Vision Transformers
Emerging Properties in Self-Supervised Vision Transformers
Mathilde Caron
Hugo Touvron
Ishan Misra
Hervé Jégou
Julien Mairal
Piotr Bojanowski
Armand Joulin
732
6,135
0
29 Apr 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
103
20
0
30 Nov 2020
On the Marginal Benefit of Active Learning: Does Self-Supervision Eat
  Its Cake?
On the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake?
Yao-Chun Chan
Mingchen Li
Samet Oymak
SSL
37
23
0
16 Nov 2020
Deep Active Learning with Augmentation-based Consistency Estimation
Deep Active Learning with Augmentation-based Consistency Estimation
SeulGi Hong
Heonjin Ha
Junmo Kim
Min-Kook Choi
57
10
0
05 Nov 2020
Cold-start Active Learning through Self-supervised Language Modeling
Cold-start Active Learning through Self-supervised Language Modeling
Michelle Yuan
Hsuan-Tien Lin
Jordan L. Boyd-Graber
188
184
0
19 Oct 2020
SCAN: Learning to Classify Images without Labels
SCAN: Learning to Classify Images without Labels
Wouter Van Gansbeke
Simon Vandenhende
Stamatios Georgoulis
Marc Proesmans
Luc Van Gool
VLMSSL
113
540
0
25 May 2020
Towards Robust and Reproducible Active Learning Using Neural Networks
Towards Robust and Reproducible Active Learning Using Neural Networks
Prateek Munjal
Nasir Hayat
Munawar Hayat
J. Sourati
Shadab Khan
UQCV
67
69
0
21 Feb 2020
A Simple Framework for Contrastive Learning of Visual Representations
A Simple Framework for Contrastive Learning of Visual Representations
Ting-Li Chen
Simon Kornblith
Mohammad Norouzi
Geoffrey E. Hinton
SSL
390
18,897
0
13 Feb 2020
FixMatch: Simplifying Semi-Supervised Learning with Consistency and
  Confidence
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Kihyuk Sohn
David Berthelot
Chun-Liang Li
Zizhao Zhang
Nicholas Carlini
E. D. Cubuk
Alexey Kurakin
Han Zhang
Colin Raffel
AAML
163
3,578
0
21 Jan 2020
Parting with Illusions about Deep Active Learning
Parting with Illusions about Deep Active Learning
Sudhanshu Mittal
Maxim Tatarchenko
Özgün Çiçek
Thomas Brox
VLM
96
59
0
11 Dec 2019
Deep Active Learning: Unified and Principled Method for Query and
  Training
Deep Active Learning: Unified and Principled Method for Query and Training
Changjian Shui
Fan Zhou
Christian Gagné
Boyu Wang
FedML
84
153
0
20 Nov 2019
Rethinking deep active learning: Using unlabeled data at model training
Rethinking deep active learning: Using unlabeled data at model training
Oriane Siméoni
Mateusz Budnik
Yannis Avrithis
G. Gravier
HAI
85
79
0
19 Nov 2019
Consistency-based Semi-supervised Active Learning: Towards Minimizing
  Labeling Cost
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost
M. Gao
Zizhao Zhang
Guo-Ding Yu
Sercan O. Arik
L. Davis
Tomas Pfister
226
200
0
16 Oct 2019
RandAugment: Practical automated data augmentation with a reduced search
  space
RandAugment: Practical automated data augmentation with a reduced search space
E. D. Cubuk
Barret Zoph
Jonathon Shlens
Quoc V. Le
MQ
260
3,505
0
30 Sep 2019
Discriminative Active Learning
Discriminative Active Learning
Daniel Gissin
Shai Shalev-Shwartz
58
178
0
15 Jul 2019
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian
  Active Learning
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
Andreas Kirsch
Joost R. van Amersfoort
Y. Gal
FedML
89
629
0
19 Jun 2019
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
Jordan T. Ash
Chicheng Zhang
A. Krishnamurthy
John Langford
Alekh Agarwal
BDLUQCV
105
777
0
09 Jun 2019
Learning Loss for Active Learning
Learning Loss for Active Learning
Donggeun Yoo
In So Kweon
UQCV
85
663
0
09 May 2019
Variational Adversarial Active Learning
Variational Adversarial Active Learning
Samarth Sinha
Sayna Ebrahimi
Trevor Darrell
GANDRLVLMSSL
140
579
0
31 Mar 2019
Diverse mini-batch Active Learning
Diverse mini-batch Active Learning
Fedor Zhdanov
59
156
0
17 Jan 2019
Addressing the Item Cold-start Problem by Attribute-driven Active
  Learning
Addressing the Item Cold-start Problem by Attribute-driven Active Learning
Y. Zhu
Jinhao Lin
S. He
Beidou Wang
Ziyu Guan
Haifeng Liu
Deng Cai
185
131
0
23 May 2018
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Richard Y. Zhang
Phillip Isola
Alexei A. Efros
Eli Shechtman
Oliver Wang
EGVM
384
11,938
0
11 Jan 2018
Deep Active Learning over the Long Tail
Deep Active Learning over the Long Tail
Yonatan Geifman
Ran El-Yaniv
3DPC
69
143
0
02 Nov 2017
Deep Bayesian Active Learning with Image Data
Deep Bayesian Active Learning with Image Data
Y. Gal
Riashat Islam
Zoubin Ghahramani
BDLUQCV
75
1,739
0
08 Mar 2017
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.3K
194,510
0
10 Dec 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
UQCVBDL
883
9,353
0
06 Jun 2015
Bayesian Active Learning for Classification and Preference Learning
Bayesian Active Learning for Classification and Preference Learning
N. Houlsby
Ferenc Huszár
Zoubin Ghahramani
M. Lengyel
130
915
0
24 Dec 2011
1