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Decorrelating Structure via Adapters Makes Ensemble Learning Practical
  for Semi-supervised Learning

Decorrelating Structure via Adapters Makes Ensemble Learning Practical for Semi-supervised Learning

8 August 2024
Jiaqi Wu
Junbiao Pang
Qingming Huang
ArXiv (abs)PDFHTML

Papers citing "Decorrelating Structure via Adapters Makes Ensemble Learning Practical for Semi-supervised Learning"

14 / 14 papers shown
Title
A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is
  Critical for Semi-supervised Classification
A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is Critical for Semi-supervised Classification
Jiaqi Wu
Junbiao Pang
Baochang Zhang
Qingming Huang
50
2
0
27 Mar 2024
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
Yidong Wang
Hao Chen
Qiang Heng
Wenxin Hou
Yue Fan
...
Marios Savvides
T. Shinozaki
Bhiksha Raj
Bernt Schiele
Xing Xie
219
278
0
15 May 2022
Humble Teachers Teach Better Students for Semi-Supervised Object
  Detection
Humble Teachers Teach Better Students for Semi-Supervised Object Detection
Yihe Tang
Weifeng Chen
Yijun Luo
Yuting Zhang
70
183
0
19 Jun 2021
Compacter: Efficient Low-Rank Hypercomplex Adapter Layers
Compacter: Efficient Low-Rank Hypercomplex Adapter Layers
Rabeeh Karimi Mahabadi
James Henderson
Sebastian Ruder
MoE
111
492
0
08 Jun 2021
AdapterFusion: Non-Destructive Task Composition for Transfer Learning
AdapterFusion: Non-Destructive Task Composition for Transfer Learning
Jonas Pfeiffer
Aishwarya Kamath
Andreas Rucklé
Kyunghyun Cho
Iryna Gurevych
CLLMoMe
141
858
0
01 May 2020
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
Ruize Wang
Duyu Tang
Nan Duan
Zhongyu Wei
Xuanjing Huang
Jianshu Ji
Guihong Cao
Daxin Jiang
Ming Zhou
KELM
116
555
0
05 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
160
3,572
0
21 Jan 2020
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
Benchmarking Neural Network Robustness to Common Corruptions and
  Perturbations
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Dan Hendrycks
Thomas G. Dietterich
OODVLM
191
3,445
0
28 Mar 2019
Probabilistic End-to-end Noise Correction for Learning with Noisy Labels
Probabilistic End-to-end Noise Correction for Learning with Noisy Labels
Kun Yi
Jianxin Wu
NoLa
68
418
0
19 Mar 2019
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks
  on Corrupted Labels
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
Lu Jiang
Zhengyuan Zhou
Thomas Leung
Li Li
Li Fei-Fei
NoLa
111
1,456
0
14 Dec 2017
Learning multiple visual domains with residual adapters
Learning multiple visual domains with residual adapters
Sylvestre-Alvise Rebuffi
Hakan Bilen
Andrea Vedaldi
OOD
176
937
0
22 May 2017
The Relative Performance of Ensemble Methods with Deep Convolutional
  Neural Networks for Image Classification
The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification
Cheng Ju
Aurélien F. Bibaut
Mark van der Laan
UQCV
77
360
0
05 Apr 2017
Stacked Hourglass Networks for Human Pose Estimation
Stacked Hourglass Networks for Human Pose Estimation
Alejandro Newell
Kaiyu Yang
Jia Deng
3DH
119
5,036
0
22 Mar 2016
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