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beyondimages

cal

A Second-Order Approach to Learning with Instance-Dependent Label Noise (CVPR'21 oral)

canlessbemore

cifar-10-100n

Human annotated noisy labels for CIFAR-10 and CIFAR-100. The website of CIFAR-N is available at http://www.noisylabels.com/.

constructiveadaptation

cores

Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)

credible-sample-elicitation

disparate-ssl

duelgan

PyTorch Implementation of ECCV 2022 paper: "DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Training".

fair-classification-with-noisy-labels

fair-eval

fair-participation-sequential-policies

fairinfl

fairness_transferability

Fairness Transferability subject to Distribution Shift

hoc

identifiability

Identifiability of Label Noise Transition Matrix

model_transferability

This repository accompanies the paper Model Transferability with Responsive Decision Subjects accepted by ICML 2023.

multi-class-peer-loss-functions

Learning with Noisy Labels by adopting a peer prediction loss function (deep learning & multi-class version).

negative-label-smoothing

[ICML2022 Long Talk] Official Pytorch implementation of "To Smooth or Not? When Label Smoothing Meets Noisy Labels"

peerloss

Learning with Noisy Labels by adopting a peer prediction loss function.

pp-bandit-feedback

This repository accompanies the paper Preformative Prediction with Bandit Feedback:Learning through Reparameterization accepted by ICML 2024.

robust-f-divergence-measures

selfsup_noisylabel

Learning from noisy labels via regularization between representations

simifeat