Alternatives and similar repositories for Meta-SPL

Users that are interested in Meta-SPL are comparing it to the libraries listed below

• wenig / spl
  ☆19Updated 5 years ago
• huaxiuyao / MLTI
  MLTI for ICLR 2022
  ☆30Updated 3 years ago
• BIT-DA / SSAN
  [ACMMM 2020] Code release for "Simultaneous Semantic Alignment Network for Heterogenous Domain Adaptation" https://arxiv.org/abs/2008.01…
  ☆31Updated 3 years ago
• TrustAGI-Lab / UDAGCN
  Python implementation of "Unsupervised Domain Adaptive Graph Convolutional Networks", WWW-20.
  ☆58Updated 4 years ago
• Luoyadan / BGNN-AAAI
  official PyTorch implementation of paper "Continual Meta-Learning with Bayesian Graph Neural Networks" (AAAI2020)
  ☆63Updated 5 years ago
• Wang-ML-Lab / GRDA
  [ICLR 2022] Graph-Relational Domain Adaptation
  ☆45Updated last year
• VITA-Group / Self-PU
  [ICML2020] "Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training" by Xuxi Chen, Wuyang Chen, Tianlong Chen, Ye Yuan, Chen Gon…
  ☆69Updated 4 years ago
• kaize0409 / Meta-PN
  PyTorch Implementation for "Meta Propagation Networks for Graph Few-shot Semi-supervised Learning" (AAAI2022)
  ☆29Updated 3 years ago
• huaxiuyao / MetaMix
  MetaMix for ICML 2021
  ☆27Updated 4 years ago
• guixianjin / noisy_label_papers
  This repository is used to record current noisy label paper in mainstream ML and CV conference and journal.
  ☆36Updated 4 years ago
• Luodian / IIB
  ☆17Updated 3 years ago
• huaxiuyao / GFL
  GFL for AAAI 2020
  ☆31Updated 4 years ago
• palm-ml / valen
  Instance-Dependent Partial Label Learning（NIPS'21）；Variational Label Enhancement for Instance-Dependent Partial Label Learning（TPAMI）
  ☆26Updated 7 months ago
• BIT-DA / EADA
  [AAAI 2022] Official Implementation of Active Learning for Domain Adaptation: An Energy-based Approach https://arxiv.org/abs/2112.01406
  ☆91Updated 2 years ago
• uvavision / Curriculum-Labeling
  [AAAI 2021] Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning
  ☆139Updated 5 years ago
• ZhiningLiu1998 / mesa
  [NeurIPS’20] ⚖️ Build powerful ensemble class-imbalanced learning models via meta-knowledge-powered resampler. | 设计元知识驱动的采样器解决类别不平衡问题
  ☆111Updated last year
• mingkai-zheng / WCL
  Weakly Supervised Contrastive Learning
  ☆45Updated 4 years ago
• gzqhappy / FGDA
  ☆19Updated 4 years ago
• ha-lins / PGCL
  The code of “Prototypical Graph Contrastive Learning”. [TNNLS 2022]
  ☆24Updated 3 years ago
• yangarbiter / multilabel-learn
  multilabel-learn: Multilabel-Classification Algorithms
  ☆36Updated 5 years ago
• ZhichenZhao / pytorch-mmoe
  ☆27Updated 5 years ago
• chauhanjatin10 / GraphsFewShot
  Few Shot Learning on Graphs
  ☆33Updated 2 years ago
• IandRover / MAML_noisy_contrasive_learner
  ☆17Updated 3 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.
  ☆62Updated 4 years ago
• wenting-zhao / mpvae
  A pytorch implementation of mpvae.
  ☆13Updated 5 years ago
• JunwenBai / MPVAE
  MPVAE: Multivariate Probit Variational AutoEncoder for Multi-Label Classification
  ☆32Updated last year
• razvancaramalau / Sequential-GCN-for-Active-Learning
  ☆54Updated 3 years ago
• lvjiaqi77 / PRODEN
  Code for the paper "Progressive Identification of True Labels for Partial-Label Learning".
  ☆52Updated 5 years ago
• liugangcode / SGIR
  [KDD'23] Source codes of "Semi-Supervised Graph Imbalanced Regression"
  ☆23Updated 6 months ago
• Deepayan137 / DeepClustering
  A pytorch implementation of the paper Unsupervised Deep Embedding for Clustering Analysis.
  ☆148Updated 6 years ago