Implementation of a state-of-art algorithm from the paper “Learning with Noisy Labels” , which is the first one providing “guarantees for risk minimization under random label noise without any assumption on the true distribution.”
Implementation of a state-of-art algorithm from the paper “Learning with Noisy Labels”[1] , which is the first one providing “guarantees for risk minimization under random label noise without any assumption on the true distribution.”
[1] “Nagarajan N, Inderjit S D, Pradeep K R, and Ambuj T. Learning with noisy labels. In Advances in neural information processing systems, pages 1196{1204, 2013.”
Implementation of a state-of-art algorithm from the paper “Learning with Noisy Labels” , which is the first one providing “guarantees for risk minimization under random label noise without any assumption on the true distribution.”