1 |
CORES* |
95.25±0.09 |
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach. (Code)
|
2 |
Divide-Mix |
95.01±0.71 |
Dividemix: Learning with noisy labels as semi-supervised learning. (Code)
|
3 |
ELR+ |
94.83±0.10 |
Early-Learning Regularization Prevents Memorization of Noisy Labels. (Code)
|
4 |
PES(semi) |
94.66±0.18 |
Understanding and Improving Early Stopping for Learning with Noisy Labels. (Code)
|
5 |
ELR |
92.38±0.64 |
Early-Learning Regularization Prevents Memorization of Noisy Labels. (Code)
|
6 |
CAL |
91.97±0.32 |
A Second-Order Approach to Learning with Instance-Dependent Label Noise. (Code)
|
7 |
Negative-LS |
91.97±0.46 |
Understanding Generalized Label Smoothing when Learning with Noisy Labels.
|
8 |
F-div |
91.64±0.34 |
When Optimizing f-Divergence is Robust with Label Noise? (Code)
|
9 |
Positive-LS |
91.57±0.07 |
Does Label Smoothing Mitigate Label Noise?
|
10 |
JoCoR |
91.44±0.05 |
Combating noisy labels by agreement: A joint training method with co-regularization. (Code)
|
11 |
CORES |
91.23±0.11 |
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach. (Code)
|
12 |
Co-Teaching |
91.20±0.13 |
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels. (Code)
|
13 |
Peer Loss |
90.75±0.25 |
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates. (Code)
|
14 |
Co-Teaching+ |
90.61±0.22 |
How does Disagreement Help Generalization against Label Corruption? (Code)
|
15 |
VolMinNet |
89.70±0.21 |
Provably end-to-end label-noise learning without anchor points. (Code)
|
16 |
T-Revision |
88.52±0.17 |
Are Anchor Points Really Indispensable in Label-Noise Learning? (Code)
|
17 |
Forward-T |
88.24±0.22 |
Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach. (Code)
|
18 |
Backward-T |
88.13±0.29 |
Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach. (Code)
|
19 |
GCE |
87.85±0.70 |
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels. (Code)
|
20 |
CE |
87.77±0.38 |
|