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Name: Xiaobo Xia
Type: User
Company: The University of Sydney
Bio: Ph.D. Candidate
Location: Sydney, Australia
Name: Xiaobo Xia
Type: User
Company: The University of Sydney
Bio: Ph.D. Candidate
Location: Sydney, Australia
A curated (most recent) list of resources for Learning with Noisy Labels
ICLR‘2021: Robust Early-learning: Hindering the Memorization of Noisy Labels
ICML‘2021: Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels
TPAMI: Classification with noisy labels by importance reweighting.
NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
ICCV'2023: Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
ICCV'2023: Holistic Label Correction for Noisy Multi-Label Classification
Official code for "HumanMAC: Masked Motion Completion for Human Motion Prediction".
NeurIPS'2020: Part-dependent Label Noise: Towards Instance-dependent Label Noise
NeurIPS'2022: Pluralistic Image Completion with Gaussian Mixture Models
Pytorch implemetations of SIGUA
Rethinking the Value of Network Pruning (Pytorch) (ICLR 2019)
Regularly Truncated M-estimators for Learning with Noisy Labels
NeurIPS'2019: Are Anchor Points Really Indispensable in Label-Noise Learning?
The code that TML group often uses
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