Ranking-based-Instance-Selection
Github repo for webly labeled learning of sound events
WACV'21: Do We Really Need Gold Samples for Sample Weighting Under Label Noise?
A Light CNN for Deep Face Representation with Noisy Labels, TIFS 2018
Code for the article "Confidence Scores Make Instance-dependent Label-noise Learning Possible", ICML'21
Las características principales de antoninbrthn/csidn son: Robust Learning Frameworks.
Las alternativas de código abierto para antoninbrthn/csidn incluyen: alibaba-edu/ranking-based-instance-selection — Ranking-based-Instance-Selection. anuragkr90/webly-labeled-sounds — Github repo for webly labeled learning of sound events. arghosh/robustmw-net — WACV'21: Do We Really Need Gold Samples for Sample Weighting Under Label Noise? automl-4paradigm/s2e — Q. Yao, H. Yang, B. Han, G. Niu, J. Kwok. Searching to Exploit Memorization Effect in Learning from Noisy Labels. ICML… awasthiabhijeet/learning-from-rules — Implementation of experiments in paper "Learning from Rules Generalizing Labeled Exemplars" to appear in ICLR2020… alfredxiangwu/lightcnn — A Light CNN for Deep Face Representation with Noisy Labels, TIFS 2018.