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BAL: Balancing Diversity and Novelty for Active Learning

BAL: Balancing Diversity and Novelty for Active Learning

26 December 2023
Jingyao Li
Pengguang Chen
Shaozuo Yu
Shu Liu
Jiaya Jia
ArXiv (abs)PDFHTMLGithub (1★)

Papers citing "BAL: Balancing Diversity and Novelty for Active Learning"

13 / 13 papers shown
Title
Masked Autoencoders Are Scalable Vision Learners
Masked Autoencoders Are Scalable Vision Learners
Kaiming He
Xinlei Chen
Saining Xie
Yanghao Li
Piotr Dollár
Ross B. Girshick
ViTTPM
480
7,837
0
11 Nov 2021
Influence Selection for Active Learning
Influence Selection for Active Learning
Zhuoming Liu
Hao Ding
Huaping Zhong
Weijia Li
Jifeng Dai
Conghui He
TDI
82
96
0
20 Aug 2021
BEiT: BERT Pre-Training of Image Transformers
BEiT: BERT Pre-Training of Image Transformers
Hangbo Bao
Li Dong
Songhao Piao
Furu Wei
ViT
297
2,845
0
15 Jun 2021
Exploring Simple Siamese Representation Learning
Exploring Simple Siamese Representation Learning
Xinlei Chen
Kaiming He
SSL
258
4,076
0
20 Nov 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
393
18,897
0
13 Feb 2020
Momentum Contrast for Unsupervised Visual Representation Learning
Momentum Contrast for Unsupervised Visual Representation Learning
Kaiming He
Haoqi Fan
Yuxin Wu
Saining Xie
Ross B. Girshick
SSL
216
12,136
0
13 Nov 2019
Variational Adversarial Active Learning
Variational Adversarial Active Learning
Samarth Sinha
Sayna Ebrahimi
Trevor Darrell
GANDRLVLMSSL
140
579
0
31 Mar 2019
Unsupervised Representation Learning by Predicting Image Rotations
Unsupervised Representation Learning by Predicting Image Rotations
Spyros Gidaris
Praveer Singh
N. Komodakis
OODSSLDRL
267
3,303
0
21 Mar 2018
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
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets,
  Atrous Convolution, and Fully Connected CRFs
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Liang-Chieh Chen
George Papandreou
Iasonas Kokkinos
Kevin Patrick Murphy
Alan Yuille
SSeg
273
18,284
0
02 Jun 2016
Fully Convolutional Networks for Semantic Segmentation
Fully Convolutional Networks for Semantic Segmentation
Evan Shelhamer
Jonathan Long
Trevor Darrell
VOSSSeg
755
37,925
0
20 May 2016
Context Encoders: Feature Learning by Inpainting
Context Encoders: Feature Learning by Inpainting
Deepak Pathak
Philipp Krahenbuhl
Jeff Donahue
Trevor Darrell
Alexei A. Efros
SSL
69
5,300
0
25 Apr 2016
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
891
9,364
0
06 Jun 2015
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