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Learning with Instance-Dependent Noisy Labels by Anchor Hallucination
  and Hard Sample Label Correction

Learning with Instance-Dependent Noisy Labels by Anchor Hallucination and Hard Sample Label Correction

10 July 2024
Po-Hsuan Huang
Chia-Ching Lin
Chih-Fan Hsu
Ming-Ching Chang
Wei-Chao Chen
    NoLa
ArXivPDFHTML

Papers citing "Learning with Instance-Dependent Noisy Labels by Anchor Hallucination and Hard Sample Label Correction"

4 / 4 papers shown
Title
Hallucination Improves Few-Shot Object Detection
Hallucination Improves Few-Shot Object Detection
Weilin Zhang
Yu-xiong Wang
ObjD
55
110
0
04 May 2021
Provably End-to-end Label-Noise Learning without Anchor Points
Provably End-to-end Label-Noise Learning without Anchor Points
Xuefeng Li
Tongliang Liu
Bo Han
Gang Niu
Masashi Sugiyama
NoLa
127
120
0
04 Feb 2021
Beyond Class-Conditional Assumption: A Primary Attempt to Combat
  Instance-Dependent Label Noise
Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise
Pengfei Chen
Junjie Ye
Guangyong Chen
Jingwei Zhao
Pheng-Ann Heng
NoLa
34
122
0
10 Dec 2020
Combating noisy labels by agreement: A joint training method with
  co-regularization
Combating noisy labels by agreement: A joint training method with co-regularization
Hongxin Wei
Lei Feng
Xiangyu Chen
Bo An
NoLa
313
498
0
05 Mar 2020
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