SREA: Self-Re-Labeling with Embedding Analysis
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Updated
Aug 26, 2021 - Python
SREA: Self-Re-Labeling with Embedding Analysis
Peerannot: classification for crowdsourced image datasets with Python
A Python Library for Biquality Learning
Effective and Robust Adversarial Training Against Data and Label Corruptions
A small experiment on classification with noisy labels
[NeurIPSW 2022] On the Ramifications of Human Label Uncertainty
Double Descent results for FCNNs on MNIST, extended by Label Noise (Reconciling Modern Machine-Learning Practice and the Classical Bias–Variance Trade-Off) [Python/PyTorch]..
Supplementary material and code for "Mitigating Label Noise through Data Ambiguation" as published at AAAI 2024.
[NeurIPS 2023] "Combating Bilateral Edge Noise for Robust Link Prediction"
Official implementation of "An Action Is Worth Multiple Words: Handling Ambiguity in Action Recognition", BMVC 2022
Implementations of different loss-correction techniques to help deep models learn under class-conditional label noise.
[NeurIPSW 2022] On the Ramifications of Human Label Uncertainty
Learning algorithms for partially-known class-conditional label noise
This is the source code for Butterfly: One-step Approach towards Wildly Unsupervised Domain Adaptation (NeurIPS'19 Workshop).
[ACM MM 2021 Oral Presentation] A unified framework for co-training-based noisy label learning methods.
Challenging label noise called BadLabel; Robust label-noise learning called Robust DivideMix
"A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?" (CVPR 2024)
Extra bits of unsanitized code for plotting, training, etc. related to our CVPR 2021 paper "Augmentation Strategies for Learning with Noisy Labels".
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