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2011.05704
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EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels
11 November 2020
Ragav Sachdeva
F. Cordeiro
Vasileios Belagiannis
Ian Reid
G. Carneiro
NoLa
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Papers citing
"EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels"
17 / 17 papers shown
Open set label noise learning with robust sample selection and margin-guided module
Knowledge-Based Systems (KBS), 2025
Yuandi Zhao
Qianxi Xia
Yang Sun
Zhijie Wen
Liyan Ma
Shihui Ying
NoLa
313
1
0
08 Jan 2025
A Survey on Open-Set Image Recognition
Qiulei Dong
Qiulei Dong
BDL
ObjD
231
10
0
25 Dec 2023
Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noise
Fahimeh Fooladgar
Minh-Son To
P. Mousavi
Purang Abolmaesumi
NoLa
200
13
0
13 Aug 2023
Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective
Renyu Zhu
Haoyu Liu
Runze Wu
Min-Hsien Lin
Tangjie Lv
Changjie Fan
Haobo Wang
NoLa
256
2
0
28 Jul 2023
Soft Curriculum for Learning Conditional GANs with Noisy-Labeled and Uncurated Unlabeled Data
IEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2023
Kai Katsumata
D. Vo
Tatsuya Harada
Hideki Nakayama
191
1
0
17 Jul 2023
Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibration
ACM Multimedia (ACM MM), 2022
Manyi Zhang
Yuxin Ren
Zihao Wang
C. Yuan
244
5
0
11 Oct 2022
Is your noise correction noisy? PLS: Robustness to label noise with two stage detection
IEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2022
Paul Albert
Eric Arazo
Tarun Kirshna
Noel E. O'Connor
Kevin McGuinness
NoLa
199
16
0
10 Oct 2022
Embedding contrastive unsupervised features to cluster in- and out-of-distribution noise in corrupted image datasets
European Conference on Computer Vision (ECCV), 2022
Paul Albert
Eric Arazo
Noel E. O'Connor
Kevin McGuinness
172
10
0
04 Jul 2022
Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives
IEEE Geoscience and Remote Sensing Magazine (GRSM), 2022
Jun Yue
Leyuan Fang
Pedram Ghamisi
Weiying Xie
Jun Li
Jocelyn Chanussot
Antonio J. Plaza
150
53
0
18 Apr 2022
SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation
Computer Vision and Pattern Recognition (CVPR), 2022
Xiaoqing Guo
Jie Liu
Tongliang Liu
Yiyuan Yuan
270
31
0
29 Mar 2022
Dropout can Simulate Exponential Number of Models for Sample Selection Techniques
RD Samsung
124
0
0
26 Feb 2022
SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise
Chen Feng
Georgios Tzimiropoulos
Ioannis Patras
NoLa
288
19
0
22 Nov 2021
NGC: A Unified Framework for Learning with Open-World Noisy Data
IEEE International Conference on Computer Vision (ICCV), 2021
Zhi-Fan Wu
Tong Wei
Jianwen Jiang
Chaojie Mao
Mingqian Tang
Yu-Feng Li
215
99
0
25 Aug 2021
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise
Neural Information Processing Systems (NeurIPS), 2021
Jianguo Huang
Lue Tao
Renchunzi Xie
Bo An
NoLa
296
101
0
21 Jun 2021
ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised Learning
Pattern Recognition (Pattern Recogn.), 2021
Ragav Sachdeva
F. Cordeiro
Vasileios Belagiannis
Ian Reid
G. Carneiro
298
42
0
21 Mar 2021
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment
Pattern Recognition (Pattern Recogn.), 2021
F. Cordeiro
Ragav Sachdeva
Vasileios Belagiannis
Ian Reid
G. Carneiro
NoLa
297
101
0
06 Mar 2021
Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020
Xiaobo Xia
Tongliang Liu
Bo Han
Nannan Wang
Jiankang Deng
Jiatong Li
Yinian Mao
NoLa
160
11
0
02 Dec 2020
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