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Active Learning on a Budget: Opposite Strategies Suit High and Low
  Budgets
v1v2v3v4 (latest)

Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets

6 February 2022
Guy Hacohen
Avihu Dekel
D. Weinshall
ArXiv (abs)PDFHTMLGithub (93★)

Papers citing "Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets"

49 / 49 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
480
0
0
18 Mar 2025
Filter Images First, Generate Instructions Later: Pre-Instruction Data Selection for Visual Instruction Tuning
Filter Images First, Generate Instructions Later: Pre-Instruction Data Selection for Visual Instruction Tuning
Bardia Safaei
Faizan Siddiqui
Jiacong Xu
Vishal M. Patel
Shao-Yuan Lo
VLM
439
1
0
10 Mar 2025
Instance-wise Supervision-level Optimization in Active Learning
Shinnosuke Matsuo
Riku Togashi
Ryoma Bise
Seiichi Uchida
Masahiro Nomura
97
0
0
09 Mar 2025
Towards Comparable Active Learning
Towards Comparable Active Learning
Thorben Werner
Johannes Burchert
Lars Schmidt-Thieme
138
0
0
24 Feb 2025
DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning
DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning
Jiaxin Guo
Cheng Chen
Shuzhen Li
Tianze Zhang
125
0
0
01 Feb 2025
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
201
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
411
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
405
3
0
10 Nov 2024
Active Learning Through a Covering Lens
Active Learning Through a Covering Lens
Ofer Yehuda
Avihu Dekel
Guy Hacohen
D. Weinshall
52
50
0
23 May 2022
A Simple Baseline for Low-Budget Active Learning
A Simple Baseline for Low-Budget Active Learning
Kossar Pourahmadi
Parsa Nooralinejad
Hamed Pirsiavash
71
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
335
895
0
15 Oct 2021
The Grammar-Learning Trajectories of Neural Language Models
The Grammar-Learning Trajectories of Neural Language Models
Leshem Choshen
Guy Hacohen
D. Weinshall
Omri Abend
85
28
0
13 Sep 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
58
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
68
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
703
6,121
0
29 Apr 2021
Boosting the Performance of Semi-Supervised Learning with Unsupervised
  Clustering
Boosting the Performance of Semi-Supervised Learning with Unsupervised Clustering
B. Lerner
Guy Shiran
D. Weinshall
SSL
49
5
0
01 Dec 2020
On Initial Pools for Deep Active Learning
On Initial Pools for Deep Active Learning
Akshay L Chandra
Sai Vikas Desai
Chaitanya Devaguptapu
V. Balasubramanian
84
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
35
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
54
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
181
184
0
19 Oct 2020
A Survey of Active Learning for Text Classification using Deep Neural
  Networks
A Survey of Active Learning for Text Classification using Deep Neural Networks
Christopher Schröder
A. Niekler
62
100
0
17 Aug 2020
Bootstrap your own latent: A new approach to self-supervised Learning
Bootstrap your own latent: A new approach to self-supervised Learning
Jean-Bastien Grill
Florian Strub
Florent Altché
Corentin Tallec
Pierre Harvey Richemond
...
M. G. Azar
Bilal Piot
Koray Kavukcuoglu
Rémi Munos
Michal Valko
SSL
377
6,833
0
13 Jun 2020
The Pitfalls of Simplicity Bias in Neural Networks
The Pitfalls of Simplicity Bias in Neural Networks
Harshay Shah
Kaustav Tamuly
Aditi Raghunathan
Prateek Jain
Praneeth Netrapalli
AAML
69
361
0
13 Jun 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
375
18,859
0
13 Feb 2020
Parting with Illusions about Deep Active Learning
Parting with Illusions about Deep Active Learning
Sudhanshu Mittal
Maxim Tatarchenko
Özgün Çiçek
Thomas Brox
VLM
93
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
79
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
82
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
206
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
241
3,503
0
30 Sep 2019
Discriminative Active Learning
Discriminative Active Learning
Daniel Gissin
Shai Shalev-Shwartz
55
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
87
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
88
776
0
09 Jun 2019
Let's Agree to Agree: Neural Networks Share Classification Order on Real
  Datasets
Let's Agree to Agree: Neural Networks Share Classification Order on Real Datasets
Guy Hacohen
Leshem Choshen
D. Weinshall
AI4TSOOD
62
57
0
26 May 2019
Learning Loss for Active Learning
Learning Loss for Active Learning
Donggeun Yoo
In So Kweon
UQCV
85
662
0
09 May 2019
On The Power of Curriculum Learning in Training Deep Networks
On The Power of Curriculum Learning in Training Deep Networks
Guy Hacohen
D. Weinshall
ODL
75
447
0
07 Apr 2019
Variational Adversarial Active Learning
Variational Adversarial Active Learning
Samarth Sinha
Sayna Ebrahimi
Trevor Darrell
GANDRLVLMSSL
122
579
0
31 Mar 2019
Diverse mini-batch Active Learning
Diverse mini-batch Active Learning
Fedor Zhdanov
59
155
0
17 Jan 2019
Theory of Curriculum Learning, with Convex Loss Functions
Theory of Curriculum Learning, with Convex Loss Functions
D. Weinshall
D. Amir
55
41
0
09 Dec 2018
Practical Obstacles to Deploying Active Learning
Practical Obstacles to Deploying Active Learning
David Lowell
Zachary Chase Lipton
Byron C. Wallace
84
111
0
12 Jul 2018
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
180
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
379
11,877
0
11 Jan 2018
Deep Active Learning over the Long Tail
Deep Active Learning over the Long Tail
Yonatan Geifman
Ran El-Yaniv
3DPC
65
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
73
1,739
0
08 Mar 2017
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
836
9,345
0
06 Jun 2015
An Introduction to Matrix Concentration Inequalities
An Introduction to Matrix Concentration Inequalities
J. Tropp
168
1,155
0
07 Jan 2015
Deep Neural Networks are Easily Fooled: High Confidence Predictions for
  Unrecognizable Images
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
Anh Totti Nguyen
J. Yosinski
Jeff Clune
AAML
171
3,275
0
05 Dec 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAttMDE
1.7K
100,479
0
04 Sep 2014
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