A curated list of resources for Learning with Noisy Labels

A curated (most recent) list of resources for Learning with Noisy Labels

Predict dense depth maps from sparse and noisy LiDAR frames guided by RGB images. (Ranked 1st place on KITTI) [MVA 2019]

Code for MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks

[ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels

AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise

The FactorGraph package provides the set of different functions to perform inference over the factor graph with continuous or discrete random variables using the belief propagation algorithm.

AAAI 2021: Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels

MIL-RBERT: A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction (BioNLP @ ACL 2020)

Code from paper High-throughput Onboard Hyperspectral Image Compression with Ground-based CNN Reconstruction

Dynamic Mixing For Speech Processing (mix-on-the-fly)

A collection of algorithms for detecting and handling label noise

SMARTboost (boosting of smooth symmetric regression trees)

Empowering Scientific Research with AI Assistance! Open Source Code for Data-Driven Dimensional Analysis.

Gaussian belief propagation solver for noisy linear systems with real coefficients and variables.

Enhanced awesome-align for low-resource languages and noise simulation: https://arxiv.org/abs/2301.09685

Attentively Embracing Noise for Robust Latent Representation in BERT (COLING 2020)

Program for non-planar camera calibration, mean square error, RANSAC algorithm, and testing with & without noisy data using extracted 3D world and 2D image feature points.

Implements the CAIRAD techique for detecting noisy values in a dataset for Weka