Robust Active Label Correction
By Jan Kremer, Fei Sha, Christian Igel. 2018
64 citations recorded by the source corpus.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
Citations in the corpus
- Continual Learning on Noisy Data Streams via Self-Purified Replay (2021, background): “Techniques that repair labels [39, 80, 50, 74, 27, 56] or directly learn them [76, 88] are also viable options for learning from noisy labeled data.”
- Self-Supervised Representation Learning by Rotation Feature Decoupling (2019, methodology): “Then the mislabeled rate can be used to handle noisy observed negative examples by various ways such as excluding examples with low confidence [40], labeling examples with high confi-dence [49, 23, 19] or reweighting examples [14, 35, 32].”
- Part-dependent Label Noise: Towards Instance-dependent Label Noise (2020, background): “Learning with noisy labels can be dated back to [4], which has recently drawn a lot of attention, especially from the deep learning community, e.g., [52, 79, 25, 13, 50, 57, 77, 37, 70, 75, 18, 41, 53, 22, 40, 56, 17, 16, 61, 60, 33, 32, 31, 21, 39, 46, 72, 66, 78].”
- Training general-purpose audio tagging networks with noisy labels and iterative self-verification (2018, background): “A related approach addressing noisy labels by active label correction has been recently proposed in [16].”
- Are Anchor Points Really Indispensable in Label-Noise Learning? (2019, background): “For example, many state-of-the-art approaches in this category are specifically designed to, e.g., select reliable examples [45, 14, 24], reweight examples [33, 15], correct labels [23, 17, 37, 32], employ side information [39, 21], and (implicitly) add regularization [13, 12, 43, 39, 21].”
- Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks (2021, background): “Even with controls for error correction [20, 49], errors can slip through.”