Topic: label-noise Goto Github
Some thing interesting about label-noise
Some thing interesting about label-noise
label-noise,A small experiment on classification with noisy labels
User: aajanki
label-noise,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.
User: alejandrods
label-noise,Implementations of different loss-correction techniques to help deep models learn under class-conditional label noise.
User: alexmirrington
Home Page: https://wandb.ai/alexmirrington/class-conditional-label-noise
label-noise,[NeurIPS 2023] Combating Bilateral Edge Noise for Robust Link Prediction
User: andrewzhou924
Home Page: https://arxiv.org/pdf/2311.01196.pdf
label-noise,A Python Library for Biquality Learning
Organization: biquality-learn
Home Page: https://biquality-learn.readthedocs.io
label-noise,Hard Sample Aware Noise Robust Learning forHistopathology Image Classification
User: bupt-ai-cz
label-noise,PyTorch Implementation of Robust Cross Entropy Loss (Loss Correction for Label Noise)
User: carlomarxdk
label-noise,SREA: Self-Re-Labeling with Embedding Analysis
User: castel44
label-noise,The official implementation of the ACM MM'2021 paper Co-learning: Learning from noisy labels with self-supervision.
User: chengtan9907
label-noise,AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise
User: chenpf1025
label-noise,AAAI 2021: Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels
User: chenpf1025
label-noise,Peerannot: classification for crowdsourced image datasets with Python
Organization: computorg
Home Page: http://computo.sfds.asso.fr/published-202402-lefort-peerannot/
label-noise,[Re] Can gradient clipping mitigate label noise? (ML Reproducibility Challenge 2020)
User: dmizr
Home Page: https://openreview.net/forum?id=TM_SgwWJA23
label-noise,for KCC 2022 Paper (Outstanding Paper Award)
User: dodant
label-noise,[Arxiv-2023] Official code for work "ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance"
User: eraseai
Home Page: https://eraseai.github.io/ERASE-page
label-noise,This is the source code for Butterfly: One-step Approach towards Wildly Unsupervised Domain Adaptation (NeurIPS'19 Workshop).
User: fengliu90
label-noise,[ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels
User: hanxunh
label-noise,Double Descent results for FCNNs on MNIST, extended by Label Noise (Reconciling Modern Machine-Learning Practice and the Classical Bias–Variance Trade-Off).
User: josch14
label-noise,Supplementary material and code for "Mitigating Label Noise through Data Ambiguation" as published at AAAI 2024.
User: julilien
label-noise,Extra bits of unsanitized code for plotting, training, etc. related to our CVPR 2021 paper "Augmentation Strategies for Learning with Noisy Labels".
User: kentonishi
Home Page: https://github.com/KentoNishi/Augmentation-for-LNL
label-noise,[CVPR 2021] Code for "Augmentation Strategies for Learning with Noisy Labels".
User: kentonishi
label-noise,Official implementation of "An Action Is Worth Multiple Words: Handling Ambiguity in Action Recognition", BMVC 2022
User: kiyoon
label-noise,Code repository for the robust active label correction paper.
User: kremerj
label-noise,Tensorflow source code for "CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise" (CVPR 2018)
User: kuanghuei
label-noise,[TPAMI2022 & NeurIPS2020] Official implementation of Self-Adaptive Training
User: layneh
label-noise,
User: mangye16
label-noise,Learning algorithms for partially-known class-conditional label noise
User: mirkobunse
label-noise,This repository implements conformal prediction methods for classification tasks that can automatically adapt to random label contamination in the calibration sample.
User: msesia
label-noise,
User: nahian-ahmed
label-noise,[NeurIPSW 2022] On the Ramifications of Human Label Uncertainty
Organization: olivesgatech
Home Page: https://arxiv.org/abs/2211.05871
label-noise,ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"
User: pxiangwu
label-noise,Contains my experiments for the Game of Deep Learning Hackathon conducted by Analytics Vidhya
User: sayakpaul
Home Page: https://datahack.analyticsvidhya.com/contest/game-of-deep-learning/
label-noise,Official PyTorch implementation of "Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data" (NeurIPS'23)
Organization: snu-mllab
label-noise,A curated list of resources for Learning with Noisy Labels
User: subeeshvasu
label-noise,Code and data for the WWW 2021 research-track paper: Typing Errors in Factual Knowledge Graphs: Severity and Possible Ways Out
Organization: u-alberta
label-noise,Human annotated noisy labels for CIFAR-10 and CIFAR-100. The website of CIFAR-N is available at http://www.noisylabels.com/.
Organization: ucsc-real
label-noise,[ICML2022 Long Talk] Official Pytorch implementation of "To Smooth or Not? When Label Smoothing Meets Noisy Labels"
Organization: ucsc-real
label-noise,A curated (most recent) list of resources for Learning with Noisy Labels
User: weijiaheng
label-noise,[ICLR2021] Official Pytorch implementation of "When Optimizing f-Divergence is Robust with Label noise"
User: weijiaheng
label-noise,[ICLR 2024] SemiReward: A General Reward Model for Semi-supervised Learning
Organization: westlake-ai
Home Page: https://arxiv.org/abs/2310.03013
label-noise,In the context of Deep Learning: What is the right way to conduct example weighting? How do you understand loss functions and so-called theorems on them?
User: xinshaoamoswang
label-noise,Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude’s Variance Matters
User: xinshaoamoswang
label-noise,Learning to Split for Automatic Bias Detection
User: yujiabao
Home Page: https://arxiv.org/abs/2204.13749
label-noise,(CVPR 2024) Pytorch implementation of “SURE: SUrvey REcipes for building reliable and robust deep networks”
User: yutingli0606
Home Page: https://yutingli0606.github.io/SURE/
label-noise,Challenging label noise called BadLabel; Robust label-noise learning called Robust DivideMix
User: zjfheart
Home Page: https://arxiv.org/abs/2305.18377
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