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TrustNet: Learning from Trusted Data Against (A)symmetric Label Noise
2021
2021 IEEE/ACM 8th International Conference on Big Data Computing, Applications and Technologies (BDCAT '21)
Big Data systems allow collecting massive datasets to feed the data hungry deep learning. Labelling these ever-bigger datasets is increasingly challenging and label errors affect even highly curated sets. This makes robustness to label noise a critical property for weakly-supervised classifiers. The related works on resilient deep networks tend to focus on a limited set of synthetic noise patterns, and with disparate views on their impacts, e.g., robustness against symmetric v.s. asymmetric
doi:10.1145/3492324.3494166
fatcat:m754v5wjibball2gjhcsrus644