Alternatives and similar repositories for lumo-implement

Users that are interested in lumo-implement are comparing it to the libraries listed below

• tmllab / 2021_NeurIPS_PES
  ☆29Updated 2 years ago
• ShiYunyi / Meta-Weight-Net_Code-Optimization
  A new code framework that uses pytorch to implement meta-learning, and takes Meta-Weight-Net as an example.
  ☆60Updated 3 years ago
• chenpf1025 / IDN
  AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise
  ☆36Updated 3 years ago
• gorkemalgan / MSLG_noisy_label
  Official code for the paper "Meta Soft Label Generation for Noisy Labels" accepted at ICPR 2020.
  ☆19Updated 4 years ago
• xiaoboxia / CDR
  ICLR‘2021: Robust Early-learning: Hindering the Memorization of Noisy Labels
  ☆76Updated 3 years ago
• microsoft / MLC
  Meta Label Correction for Noisy Label Learning
  ☆85Updated 2 years ago
• AlanChou / Super-Loss
  PyTorch implementation of the paper "SuperLoss: A Generic Loss for Robust Curriculum Learning" in NIPS 2020.
  ☆29Updated 4 years ago
• ShuoYang-1998 / BLTM
  [ICML'2022] Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network
  ☆19Updated 2 years ago
• TinfoilHat0 / Learning-to-Reweight-Examples-for-Robust-Deep-Learning-with-PyTorch-Higher
  An implementation of the paper "Learning to Reweight Examples for Robust Deep Learning" from ICML 2018 with PyTorch and Higher.
  ☆28Updated 2 years ago
• bhanML / label-noise-papers
  An update-to-date list for papers related with label-noise representation learning is here.
  ☆89Updated 3 years ago
• xiaoboxia / Part-dependent-label-noise
  NeurIPS'2020: Part-dependent Label Noise: Towards Instance-dependent Label Noise
  ☆60Updated 4 years ago
• VITA-Group / SDCLR
  [ICML 2021] “ Self-Damaging Contrastive Learning”, Ziyu Jiang, Tianlong Chen, Bobak Mortazavi, Zhangyang Wang
  ☆63Updated 3 years ago
• ZhenWang-PhD / Training-Noise-Robust-Deep-Neural-Networks-via-Meta-Learning
  Code for CVPR2020 ‘Training Noise Robust Deep Neural Networks via Meta-Learning’
  ☆20Updated 4 years ago
• salesforce / MoPro
  MoPro: Webly Supervised Learning
  ☆87Updated last month
• xingruiyu / coteaching_plus
  ICML'19 How does Disagreement Help Generalization against Label Corruption?
  ☆84Updated 5 years ago
• xiaoboxia / Classification-with-noisy-labels-by-importance-reweighting
  TPAMI: Classification with noisy labels by importance reweighting.
  ☆40Updated 5 years ago
• takashiishida / comp
  [NeurIPS 2017] [ICML 2019] Code for complementary-label learning
  ☆48Updated last year
• haochenglouis / cores
  Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)
  ☆34Updated 4 years ago
• WuYichen-97 / Learning-to-Purify-Noisy-Labels-via-Meta-Soft-Label-Corrector
  [AAAI 21] Utilizing meta-learning to correct the noisy labels.
  ☆15Updated 4 years ago
• UCSC-REAL / cores
  Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)
  ☆37Updated 4 years ago
• haebeom-lee / l2b
  Tensorflow implementation of "Learning to Balance: Bayesian Meta-learning for Imbalanced and Out-of-distribution Tasks" (ICLR 2020 oral)
  ☆101Updated 4 years ago
• ShikunLi / Sel-CL
  CVPR 2022: Selective-Supervised Contrastive Learning with Noisy Labels
  ☆93Updated 3 years ago
• kibok90 / imix
  ICLR 2021 i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning
  ☆78Updated last year
• Luoyadan / BGNN-AAAI
  official PyTorch implementation of paper "Continual Meta-Learning with Bayesian Graph Neural Networks" (AAAI2020)
  ☆63Updated 4 years ago
• xiaoboxia / T-Revision
  NeurIPS'2019: Are Anchor Points Really Indispensable in Label-Noise Learning?
  ☆98Updated 3 years ago
• xjtushujun / meta-weight-net
  NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).
  ☆288Updated 3 years ago
• x-zho14 / MAPLE
  Code for Model Agnostic Sample Reweighting for Out-of-Distribution Learning
  ☆44Updated 2 years ago
• liyiying / Feature_Critic
  Feature-Critic Networks for Heterogeneous Domain Generalisation
  ☆53Updated 6 years ago
• pxiangwu / PLC
  ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"
  ☆43Updated 2 years ago
• lvjiaqi77 / PRODEN
  Code for the paper "Progressive Identification of True Labels for Partial-Label Learning".
  ☆51Updated 4 years ago