A curated list of resources for Learning with Noisy Labels
#计算机科学#A curated (most recent) list of resources for Learning with Noisy Labels
#计算机科学#Predict dense depth maps from sparse and noisy LiDAR frames guided by RGB images. (Ranked 1st place on KITTI) [MVA 2019]
#计算机科学#Code for MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks
#计算机科学#[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)
#计算机科学#Code from paper High-throughput Onboard Hyperspectral Image Compression with Ground-based CNN Reconstruction
Dynamic Mixing For Speech Processing (mix-on-the-fly)
#计算机科学#A collection of algorithms for detecting and handling label noise
Empowering Scientific Research with AI Assistance! Open Source Code for Data-Driven Dimensional Analysis.
#计算机科学#SMARTboost (boosting of smooth symmetric regression trees)
Gaussian belief propagation solver for noisy linear systems with real coefficients and variables.
Attentively Embracing Noise for Robust Latent Representation in BERT (COLING 2020)
Enhanced awesome-align for low-resource languages and noise simulation: https://arxiv.org/abs/2301.09685
Official implementation of our paper: "COLA: Leveraging Local and Global Relationships for Corrupted Label Detection"
A tool to determine uncertainty in numeric integrals of noisy x-y data.
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.