Alternatives and similar repositories for SPamCo:

Users that are interested in SPamCo are comparing it to the libraries listed below

• bhanML / SIGUA
  ICML'20: SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
  ☆15Updated 4 years ago
• Sunarker / Safeguarded-Dynamic-Label-Regression-for-Noisy-Supervision
  ☆9Updated last year
• Flowerfan / Open-Reid
  Self-Paced Multi-view Co-training for person re-id experiment
  ☆30Updated 3 years ago
• LARS-research / S2E
  Q. Yao, H. Yang, B. Han, G. Niu, J. Kwok. Searching to Exploit Memorization Effect in Learning from Noisy Labels. ICML 2020
  ☆21Updated 4 years ago
• dvornikita / SUR
  Code for the paper "Selecting Relevant Features from a Universal Representation for Few-shot Classification"
  ☆41Updated 3 years ago
• Sha-Lab / aCASTLE
  The code repository for "Learning Classifier Synthesis for Generalized Few-shot Learning"
  ☆22Updated 3 years ago
• Raymond-sci / PICA
  Official Pytorch Implementation for CVPR'20 paper: Deep Semantic Clustering by Partition Confidence Maximisation
  ☆58Updated 4 years ago
• liulu112601 / Gated-Propagation-Net
  NeurIPS 2019 : Learning to Propagate for Graph Meta-Learning
  ☆36Updated 5 years ago
• AhmedAyad89 / Consitent-Prototypical-Networks-Semi-Supervised-Few-Shot-Learning
  ☆19Updated 5 years ago
• szkocot / Adapting-Auxiliary-Losses-Using-Gradient-Similarity
  Implementation "Adapting Auxiliary Losses Using Gradient Similarity" article
  ☆32Updated 6 years ago
• tonyabracadabra / Deep-Subspace-Clustering
  Subspace clustering through the deep learning approach
  ☆22Updated 8 years ago
• zcrwind / tgg-pytorch
  The source code of our ACM MM 2019 paper "TGG: Transferable Graph Generation for Zero-shot and Few-shot Learning".
  ☆25Updated 5 years ago
• bhanML / coteaching_plus
  ICML'19: How does Disagreement Help Generalization against Label Corruption?
  ☆21Updated 5 years ago
• cadurosar / graph_kd
  Graph Knowledge Distillation
  ☆13Updated 5 years ago
• k-r-allen / imp
  infinite mixture prototypes for few-shot learning
  ☆51Updated 2 years ago
• qy-feng / Margin-Openset
  [ICCV2019] Attract or Distract: Exploit the Margin of Open Set
  ☆35Updated 4 years ago
• guyuchao / VAE-cFlow-ZSL
  Implementation for "Generalized Zero-Shot Learning via VAE-Conditioned Generative Flow" （https://arxiv.org/abs/2009.00303）
  ☆24Updated 4 years ago
• yaohungt / Self_Supervised_Learning_Multiview
  ☆42Updated 4 years ago
• XLearning-SCU / 2021-CVPR-MvCLN
  PyTorch implementation for Partially View-aligned Representation Learning with Noise-robust Contrastive Loss (CVPR 2021)
  ☆46Updated 2 years ago
• hbdat / cvpr20_IMCL
  Interactive Multi-Label CNN Learning with Partial Labels @ CVPR20
  ☆20Updated 3 years ago
• Dyfine / SphericalEmbedding
  official pytorch implementation of "Deep Metric Learning with Spherical Embedding", NeurIPS 2020
  ☆41Updated 4 years ago
• fuxianghuang1 / PWCF
  Published in CVPR 2020; matlab codes
  ☆22Updated 5 months ago
• mancinimassimiliano / latent_domains_DA
  Official Caffe implementation of Boosting Domain Adaptation by Discovering Latent Domains.
  ☆23Updated 4 years ago
• ricvolpi / domain-shift-robustness
  Code for the paper "Addressing Model Vulnerability to Distributional Shifts over Image Transformation Sets", ICCV 2019
  ☆27Updated 4 years ago
• kreimanlab / VariationalPrototypeReplaysCL
  Continual learning using variational prototype replays
  ☆9Updated 4 years ago
• crashmoon / MAML-Pytorch-Multi-GPUs
  pytorch maml with Multi-GPUs, fast and simplest implementation
  ☆13Updated 4 years ago
• tianyizhou / DoCL
  Paper and Code for "Curriculum Learning by Optimizing Learning Dynamics" (AISTATS 2021)
  ☆19Updated 3 years ago
• AntreasAntoniou / FewShotContinualLearningDataProvider
  The original code for the data providers and the datasets of the paper "Defining Benchmarks for Continual Few-Shot Learning".
  ☆16Updated 4 years ago
• PaulAlbert31 / ReLaB
  Official repository for Reliable Label Bootstrapping
  ☆19Updated last year
• Haoang97 / TOHAN
  Source code for NeurIPS 2021 paper "TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation".
  ☆9Updated 2 years ago