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Pseudo-Labeling Curriculum for Unsupervised Domain Adaptation

Pseudo-Labeling Curriculum for Unsupervised Domain Adaptation

1 August 2019
Jaehoon Choi
Minki Jeong
Taekyung Kim
Changick Kim
ArXivPDFHTML

Papers citing "Pseudo-Labeling Curriculum for Unsupervised Domain Adaptation"

17 / 17 papers shown
Title
CNG-SFDA: Clean-and-Noisy Region Guided Online-Offline Source-Free
  Domain Adaptation
CNG-SFDA: Clean-and-Noisy Region Guided Online-Offline Source-Free Domain Adaptation
Hyeonwoo Cho
Chanmin Park
Donghee Kim
Jinyoung Kim
Won Hwa Kim
TTA
38
0
0
26 Jan 2024
Strong-Weak Integrated Semi-supervision for Unsupervised Single and
  Multi Target Domain Adaptation
Strong-Weak Integrated Semi-supervision for Unsupervised Single and Multi Target Domain Adaptation
Xiaohu Lu
H. Radha
33
0
0
12 Sep 2023
C-SFDA: A Curriculum Learning Aided Self-Training Framework for
  Efficient Source Free Domain Adaptation
C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation
Nazmul Karim
Niluthpol Chowdhury Mithun
Abhinav Rajvanshi
Han-Pang Chiu
S. Samarasekera
Nazanin Rahnavard
TTA
23
56
0
30 Mar 2023
Learning to Learn: How to Continuously Teach Humans and Machines
Learning to Learn: How to Continuously Teach Humans and Machines
Parantak Singh
You Li
Ankur Sikarwar
Weixian Lei
Daniel Gao
Morgan B. Talbot
Ying Sun
Mike Zheng Shou
Gabriel Kreiman
Mengmi Zhang
CLL
20
6
0
28 Nov 2022
Multiple Instance Learning via Iterative Self-Paced Supervised
  Contrastive Learning
Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning
Kangning Liu
Weicheng Zhu
Yiqiu Shen
Sheng Liu
N. Razavian
Krzysztof J. Geras
C. Fernandez‐Granda
SSL
33
25
0
17 Oct 2022
Dual-Curriculum Teacher for Domain-Inconsistent Object Detection in
  Autonomous Driving
Dual-Curriculum Teacher for Domain-Inconsistent Object Detection in Autonomous Driving
L. Yu
Yifan Zhang
Lanqing Hong
Fei Chen
Zhenguo Li
48
3
0
17 Oct 2022
Towards Adaptive Unknown Authentication for Universal Domain Adaptation
  by Classifier Paradox
Towards Adaptive Unknown Authentication for Universal Domain Adaptation by Classifier Paradox
Yunyun Wang
Yaojie Liu
Songcan Chen
20
1
0
10 Jul 2022
Boosting Unsupervised Domain Adaptation with Soft Pseudo-label and
  Curriculum Learning
Boosting Unsupervised Domain Adaptation with Soft Pseudo-label and Curriculum Learning
Shengjia Zhang
Tiancheng Lin
Yi Tian Xu
30
5
0
03 Dec 2021
CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation
CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation
Tongkun Xu
Weihua Chen
Pichao Wang
Fan Wang
Hao Li
Rong Jin
ViT
61
216
0
13 Sep 2021
Randomized Histogram Matching: A Simple Augmentation for Unsupervised
  Domain Adaptation in Overhead Imagery
Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery
Can Yaris
Kaleb Kassaw
Bohao Huang
Kyle Bradbury
Jordan M. Malof
23
20
0
28 Apr 2021
GANcraft: Unsupervised 3D Neural Rendering of Minecraft Worlds
GANcraft: Unsupervised 3D Neural Rendering of Minecraft Worlds
Jinwei Gu
Arun Mallya
Serge Belongie
Ming Liu
36
119
0
15 Apr 2021
Semi-Supervised Domain Adaptation via Selective Pseudo Labeling and
  Progressive Self-Training
Semi-Supervised Domain Adaptation via Selective Pseudo Labeling and Progressive Self-Training
Yoonhyung Kim
Changick Kim
19
8
0
01 Apr 2021
ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object
  Detection
ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection
Jihan Yang
Shaoshuai Shi
Zhe Wang
Hongsheng Li
Xiaojuan Qi
3DPC
21
187
0
09 Mar 2021
A Survey on Curriculum Learning
A Survey on Curriculum Learning
Xin Eric Wang
Yudong Chen
Wenwu Zhu
SyDa
32
22
0
25 Oct 2020
Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive
  Object Re-ID
Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID
Yixiao Ge
Feng Zhu
Dapeng Chen
Rui Zhao
Hongsheng Li
VLM
36
556
0
04 Jun 2020
Do We Really Need to Access the Source Data? Source Hypothesis Transfer
  for Unsupervised Domain Adaptation
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
Jian Liang
Dapeng Hu
Jiashi Feng
50
1,210
0
20 Feb 2020
Mean teachers are better role models: Weight-averaged consistency
  targets improve semi-supervised deep learning results
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen
Harri Valpola
OOD
MoMe
273
1,275
0
06 Mar 2017
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