How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
Q. Yao, H. Yang, B. Han, G. Niu, J. Kwok. Searching to Exploit Memorization Effect in Learning from Noisy Labels. ICML 2020
The main features of automl-4paradigm/s2e are: Robust Learning Frameworks.
Open-source alternatives to automl-4paradigm/s2e include: alibaba-edu/ranking-based-instance-selection — Ranking-based-Instance-Selection. antoninbrthn/csidn — Code for the article "Confidence Scores Make Instance-dependent Label-noise Learning Possible", ICML'21. 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? 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.
Ranking-based-Instance-Selection
Code for the article "Confidence Scores Make Instance-dependent Label-noise Learning Possible", ICML'21
Github repo for webly labeled learning of sound events
A Light CNN for Deep Face Representation with Noisy Labels, TIFS 2018