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MixMatch: A Holistic Approach to Semi-Supervised Learning

MixMatch: A Holistic Approach to Semi-Supervised Learning

6 May 2019
David Berthelot
Nicholas Carlini
Ian Goodfellow
Nicolas Papernot
Avital Oliver
Colin Raffel
ArXivPDFHTML

Papers citing "MixMatch: A Holistic Approach to Semi-Supervised Learning"

50 / 554 papers shown
Title
Model-Based Deep Learning
Model-Based Deep Learning
Nir Shlezinger
Jay Whang
Yonina C. Eldar
A. Dimakis
28
315
0
15 Dec 2020
Teach me to segment with mixed supervision: Confident students become
  masters
Teach me to segment with mixed supervision: Confident students become masters
Jose Dolz
Christian Desrosiers
Ismail Ben Ayed
16
25
0
15 Dec 2020
Iterative label cleaning for transductive and semi-supervised few-shot
  learning
Iterative label cleaning for transductive and semi-supervised few-shot learning
Michalis Lazarou
Tania Stathaki
Yannis Avrithis
40
61
0
14 Dec 2020
Source Data-absent Unsupervised Domain Adaptation through Hypothesis
  Transfer and Labeling Transfer
Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer
Jian Liang
Dapeng Hu
Yunbo Wang
Ran He
Jiashi Feng
151
250
0
14 Dec 2020
Few-Shot Segmentation Without Meta-Learning: A Good Transductive
  Inference Is All You Need?
Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?
Malik Boudiaf
H. Kervadec
Imtiaz Masud Ziko
Pablo Piantanida
Ismail Ben Ayed
Jose Dolz
VLM
177
188
0
11 Dec 2020
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
22
5
0
01 Dec 2020
They are Not Completely Useless: Towards Recycling Transferable
  Unlabeled Data for Class-Mismatched Semi-Supervised Learning
They are Not Completely Useless: Towards Recycling Transferable Unlabeled Data for Class-Mismatched Semi-Supervised Learning
Zhuo Huang
Ying Tai
Chengjie Wang
Jian Yang
Chen Gong
31
23
0
27 Nov 2020
Regularization with Latent Space Virtual Adversarial Training
Regularization with Latent Space Virtual Adversarial Training
Genki Osada
Budrul Ahsan
Revoti Prasad Bora
Takashi Nishide
30
14
0
26 Nov 2020
Grafit: Learning fine-grained image representations with coarse labels
Grafit: Learning fine-grained image representations with coarse labels
Hugo Touvron
Alexandre Sablayrolles
Matthijs Douze
Matthieu Cord
Hervé Jégou
SSL
34
68
0
25 Nov 2020
KeepAugment: A Simple Information-Preserving Data Augmentation Approach
KeepAugment: A Simple Information-Preserving Data Augmentation Approach
Chengyue Gong
Dilin Wang
Meng Li
Vikas Chandra
Qiang Liu
33
113
0
23 Nov 2020
CoMatch: Semi-supervised Learning with Contrastive Graph Regularization
CoMatch: Semi-supervised Learning with Contrastive Graph Regularization
Junnan Li
Caiming Xiong
Guosheng Lin
SSL
21
252
0
23 Nov 2020
SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO
  Approximations
SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations
H. Feng
Kezhi Kong
Minghao Chen
Tianye Zhang
Minfeng Zhu
Wei Chen
VLM
DRL
42
24
0
21 Nov 2020
Towards Domain-Agnostic Contrastive Learning
Towards Domain-Agnostic Contrastive Learning
Vikas Verma
Minh-Thang Luong
Kenji Kawaguchi
Hieu H. Pham
Quoc V. Le
SSL
15
115
0
09 Nov 2020
A Survey of Label-noise Representation Learning: Past, Present and
  Future
A Survey of Label-noise Representation Learning: Past, Present and Future
Bo Han
Quanming Yao
Tongliang Liu
Gang Niu
Ivor W. Tsang
James T. Kwok
Masashi Sugiyama
NoLa
24
158
0
09 Nov 2020
UFO$^2$: A Unified Framework towards Omni-supervised Object Detection
UFO2^22: A Unified Framework towards Omni-supervised Object Detection
Zhongzheng Ren
Zhiding Yu
Xiaodong Yang
Xuan Li
A. Schwing
Jan Kautz
ObjD
198
35
0
21 Oct 2020
RDIS: Random Drop Imputation with Self-Training for Incomplete Time
  Series Data
RDIS: Random Drop Imputation with Self-Training for Incomplete Time Series Data
Taehyean Choi
Ji-Su Kang
Jong-Hwan Kim
SyDa
AI4TS
22
21
0
20 Oct 2020
Combining Ensembles and Data Augmentation can Harm your Calibration
Combining Ensembles and Data Augmentation can Harm your Calibration
Yeming Wen
Ghassen Jerfel
Rafael Muller
Michael W. Dusenberry
Jasper Snoek
Balaji Lakshminarayanan
Dustin Tran
UQCV
32
63
0
19 Oct 2020
Semi-supervised Batch Active Learning via Bilevel Optimization
Semi-supervised Batch Active Learning via Bilevel Optimization
Zalan Borsos
Marco Tagliasacchi
Andreas Krause
29
23
0
19 Oct 2020
Unsupervised Semantic Aggregation and Deformable Template Matching for
  Semi-Supervised Learning
Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning
Tao Han
Junyu Gao
Yuan. Yuan
Qi. Wang
22
26
0
12 Oct 2020
How Does Mixup Help With Robustness and Generalization?
How Does Mixup Help With Robustness and Generalization?
Linjun Zhang
Zhun Deng
Kenji Kawaguchi
Amirata Ghorbani
James Zou
AAML
20
244
0
09 Oct 2020
InstaHide: Instance-hiding Schemes for Private Distributed Learning
InstaHide: Instance-hiding Schemes for Private Distributed Learning
Yangsibo Huang
Zhao Song
Keqin Li
Sanjeev Arora
FedML
PICV
14
150
0
06 Oct 2020
CO2: Consistent Contrast for Unsupervised Visual Representation Learning
CO2: Consistent Contrast for Unsupervised Visual Representation Learning
Chen Wei
Huiyu Wang
Wei Shen
Alan Yuille
SSL
36
62
0
05 Oct 2020
A Large Multi-Target Dataset of Common Bengali Handwritten Graphemes
A Large Multi-Target Dataset of Common Bengali Handwritten Graphemes
Samiul Alam
Tahsin Reasat
Asif Sushmit
Sadi Mohammad Siddiquee
Fuad Rahman
Mahady Hasan
Ahmed Imtiaz Humayun
10
21
0
01 Oct 2020
GraphXCOVID: Explainable Deep Graph Diffusion Pseudo-Labelling for
  Identifying COVID-19 on Chest X-rays
GraphXCOVID: Explainable Deep Graph Diffusion Pseudo-Labelling for Identifying COVID-19 on Chest X-rays
Angelica I Aviles-Rivero
P. Sellars
Carola-Bibiane Schönlieb
Nicolas Papadakis
DiffM
MedIm
SSL
17
30
0
30 Sep 2020
Semi-supervised sequence classification through change point detection
Semi-supervised sequence classification through change point detection
Nauman Ahad
Mark A. Davenport
SSL
AI4TS
19
7
0
24 Sep 2020
Enhancing Mixup-based Semi-Supervised Learning with Explicit Lipschitz
  Regularization
Enhancing Mixup-based Semi-Supervised Learning with Explicit Lipschitz Regularization
P. Gyawali
S. Ghimire
Linwei Wang
AAML
23
7
0
23 Sep 2020
Contrastive and Generative Graph Convolutional Networks for Graph-based
  Semi-Supervised Learning
Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning
Sheng Wan
Shirui Pan
Jian Yang
Chen Gong
SSL
22
137
0
15 Sep 2020
Manifold attack
Manifold attack
K. Tran
Fred-Maurice Ngole-Mboula
Jean-Luc Starck
AAML
OOD
21
0
0
13 Sep 2020
Beyond Point Estimate: Inferring Ensemble Prediction Variation from
  Neuron Activation Strength in Recommender Systems
Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems
Zhe Chen
Yuyan Wang
Dong Lin
D. Cheng
Lichan Hong
Ed H. Chi
Claire Cui
28
16
0
17 Aug 2020
Self-Path: Self-supervision for Classification of Pathology Images with
  Limited Annotations
Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations
Navid Alemi Koohbanani
Balagopal Unnikrishnan
S. Khurram
Pavitra Krishnaswamy
Nasir M. Rajpoot
SSL
22
164
0
12 Aug 2020
Guided Collaborative Training for Pixel-wise Semi-Supervised Learning
Guided Collaborative Training for Pixel-wise Semi-Supervised Learning
Zhanghan Ke
Di Qiu
Kaican Li
Qiong Yan
Rynson W. H. Lau
28
247
0
12 Aug 2020
Learning from a Complementary-label Source Domain: Theory and Algorithms
Learning from a Complementary-label Source Domain: Theory and Algorithms
Yiyang Zhang
Feng Liu
Zhen Fang
Bo Yuan
Guangquan Zhang
Jie Lu
28
70
0
04 Aug 2020
Mixup-CAM: Weakly-supervised Semantic Segmentation via Uncertainty
  Regularization
Mixup-CAM: Weakly-supervised Semantic Segmentation via Uncertainty Regularization
Yu-Ting Chang
Qiaosong Wang
Wei-Chih Hung
Robinson Piramuthu
Yi-Hsuan Tsai
Ming-Hsuan Yang
UQCV
WSOL
22
34
0
03 Aug 2020
Robust and Generalizable Visual Representation Learning via Random
  Convolutions
Robust and Generalizable Visual Representation Learning via Random Convolutions
Zhenlin Xu
Deyi Liu
Junlin Yang
Colin Raffel
Marc Niethammer
OOD
AAML
49
190
0
25 Jul 2020
Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning
Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning
Qing Yu
Daiki Ikami
Go Irie
Kiyoharu Aizawa
17
128
0
22 Jul 2020
NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural
  Architecture Search
NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search
Zhichao Lu
Kalyanmoy Deb
E. Goodman
W. Banzhaf
Vishnu Naresh Boddeti
26
144
0
20 Jul 2020
Distribution Aligning Refinery of Pseudo-label for Imbalanced
  Semi-supervised Learning
Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning
Jaehyung Kim
Youngbum Hur
Sejun Park
Eunho Yang
Sung Ju Hwang
Jinwoo Shin
17
159
0
17 Jul 2020
DACS: Domain Adaptation via Cross-domain Mixed Sampling
DACS: Domain Adaptation via Cross-domain Mixed Sampling
Wilhelm Tranheden
Viktor Olsson
Juliano Pinto
Lennart Svensson
21
351
0
17 Jul 2020
ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised
  Learning
ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning
Viktor Olsson
Wilhelm Tranheden
Juliano Pinto
Lennart Svensson
24
330
0
15 Jul 2020
A Survey of Privacy Attacks in Machine Learning
A Survey of Privacy Attacks in Machine Learning
M. Rigaki
Sebastian Garcia
PILM
AAML
39
213
0
15 Jul 2020
Active Crowd Counting with Limited Supervision
Active Crowd Counting with Limited Supervision
Zhen Zhao
Miaojing Shi
Xiaoxiao Zhao
Li Li
27
47
0
13 Jul 2020
Remix: Rebalanced Mixup
Remix: Rebalanced Mixup
Hsin-Ping Chou
Shih-Chieh Chang
Jia-Yu Pan
Wei Wei
Da-Cheng Juan
36
231
0
08 Jul 2020
Not All Unlabeled Data are Equal: Learning to Weight Data in
  Semi-supervised Learning
Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning
Zhongzheng Ren
Raymond A. Yeh
A. Schwing
36
95
0
02 Jul 2020
Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for
  Improved Generalization
Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization
Sang Michael Xie
Tengyu Ma
Percy Liang
30
13
0
29 Jun 2020
Unlabelled Data Improves Bayesian Uncertainty Calibration under
  Covariate Shift
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
Alex J. Chan
Ahmed Alaa
Zhaozhi Qian
M. Schaar
UQCV
BDL
OOD
28
38
0
26 Jun 2020
AdvAug: Robust Adversarial Augmentation for Neural Machine Translation
AdvAug: Robust Adversarial Augmentation for Neural Machine Translation
Yong Cheng
Lu Jiang
Wolfgang Macherey
Jacob Eisenstein
29
115
0
21 Jun 2020
Boosting Active Learning for Speech Recognition with Noisy
  Pseudo-labeled Samples
Boosting Active Learning for Speech Recognition with Noisy Pseudo-labeled Samples
Jihwan Bang
Heesu Kim
Y. Yoo
Jung-Woo Ha
9
2
0
19 Jun 2020
Tent: Fully Test-time Adaptation by Entropy Minimization
Tent: Fully Test-time Adaptation by Entropy Minimization
Dequan Wang
Evan Shelhamer
Shaoteng Liu
Bruno A. Olshausen
Trevor Darrell
OOD
40
53
0
18 Jun 2020
MixMOOD: A systematic approach to class distribution mismatch in
  semi-supervised learning using deep dataset dissimilarity measures
MixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measures
Saul Calderon-Ramirez
Luis Oala
J. Torrents-Barrena
Shengxiang-Yang
Armaghan Moemeni
Wojciech Samek
Miguel A. Molina-Cabello
22
10
0
14 Jun 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
119
6,655
0
13 Jun 2020
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