RSS-MGM is a label-noise training method that unions small-loss and high-confidence sample selection and uses margin functions to split noisy samples into open-set (discarded) and closed-set (pseudo-labeled) groups, with modest accuracy gains on several image benchmarks.
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Open set label noise learning with robust sample selection and margin-guided module
RSS-MGM is a label-noise training method that unions small-loss and high-confidence sample selection and uses margin functions to split noisy samples into open-set (discarded) and closed-set (pseudo-labeled) groups, with modest accuracy gains on several image benchmarks.