Combinatorial Inference against Label Noise

NeurIPS 2019 Paul Hongsuck SeoGeeho KimBohyung Han

Label noise is one of the critical sources that degrade generalization performance of deep neural networks significantly. To handle the label noise issue in a principled way, we propose a unique classification framework of constructing multiple models in heterogeneous coarse-grained meta-class spaces and making joint inference of the trained models for the final predictions in the original (base) class space... (read more)

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