A curated list of resources for Learning with Noisy Labels
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Updated
May 3, 2024
A curated 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
A curated (most recent) list of resources for Learning with Noisy Labels
[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
MIL-RBERT: A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction (BioNLP @ ACL 2020)
Gaussian belief propagation solver for noisy linear systems with real coefficients and variables.
Code from paper High-throughput Onboard Hyperspectral Image Compression with Ground-based CNN Reconstruction
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.
A collection of algorithms for detecting and handling label noise
AAAI 2021: Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels
SMARTboost (boosting of smooth symmetric regression trees)
Dynamic Mixing For Speech Processing (mix-on-the-fly)
Kalman Filters are used for state estimation in control systems. This repository includes an implementation of the algorithm in Python and also a Jupyter Notebook for testing in real data for altitude estimation of a quadrotor
Empowering Scientific Research with AI Assistance! Open Source Code for Data-Driven Dimensional Analysis.
Analysis of robust classification algorithms for overcoming class-dependant labelling noise: Forward, Importance Reweighting and T-revision. We demonstrate methods for estimating the transition matrix in order to obtain better classifier performance when working with noisy data.
Enhanced awesome-align for low-resource languages and noise simulation: https://arxiv.org/abs/2301.09685
Least squares and recursive least squares implementation. 2D line fit to noisy data.
LeNet5 architecture implementation using pytorch, network parameter optimization and performance evaluation on dataset with Symmetric Label Noise
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