Alternatives and similar repositories for GAL

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

• danielzuegner / robust-gcn
  Implementation of the paper "Certifiable Robustness and Robust Training for Graph Convolutional Networks".
  ☆43Updated 4 years ago
• alvinchangw / IGAM_CVPR2020
  Implementation for What it Thinks is Important is Important: Robustness Transfers through Input Gradients (CVPR 2020 Oral)
  ☆16Updated 2 years ago
• alvinchangw / CARA_EMNLP2020
  Implementation for Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder (EMNLP-Findings 2020)
  ☆15Updated 4 years ago
• xuanqing94 / AdvSSL
  A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning
  ☆12Updated 5 years ago
• bluer555 / KernelGCN
  Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs
  ☆34Updated 5 years ago
• bhanML / SIGUA
  ICML'20: SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
  ☆15Updated 4 years ago
• chentingpc / gfn
  Code for "Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification"
  ☆52Updated 5 years ago
• abojchevski / node_embedding_attack
  Adversarial Attacks on Node Embeddings via Graph Poisoning
  ☆59Updated 5 years ago
• enyac-group / Renofeation
  ☆19Updated 4 years ago
• tangxianfeng / PA-GNN
  Implementation of paper "Transferring Robustness for Graph Neural Network Against Poisoning Attacks".
  ☆20Updated 5 years ago
• yang-han / P-reg
  Rethinking Graph Regularization for Graph Neural Networks (AAAI2021)
  ☆34Updated 4 years ago
• houshd / TS_Adv
  Codebase for the paper "Adversarial Attacks on Time Series"
  ☆19Updated 6 years ago
• mndu / guided-feature-inversion
  PyTorch code for KDD 18 paper: Towards Explanation of DNN-based Prediction with Guided Feature Inversion
  ☆21Updated 6 years ago
• inspire-group / robustness-via-transport
  ☆12Updated 5 years ago
• tginart / deletion-efficient-kmeans
  Research prototype of deletion efficient k-means algorithms
  ☆24Updated 5 years ago
• ihollywhy / DistillGCN.PyTorch
  Source code for "Distilling Knowledge From Graph Convolutional Networks", CVPR'20
  ☆57Updated 2 years ago
• davidstutz / disentangling-robustness-generalization
  CVPR'19 experiments with (on-manifold) adversarial examples.
  ☆45Updated 5 years ago
• haebeom-lee / metadrop
  Tensorflow implementation of "Meta Dropout: Learning to Perturb Latent Features for Generalization" (ICLR 2020)
  ☆27Updated 5 years ago
• yifan-h / CS-GNN
  Measuring and Improving the Use of Graph Information in Graph Neural Networks
  ☆82Updated 11 months ago
• MadryLab / DebuggableDeepNetworks
  ☆38Updated 4 years ago
• facebookresearch / private_prediction
  The code reproduces the results of the experiments in the paper. In particular, it performs experiments in which machine-learning models …
  ☆20Updated 3 years ago
• LFhase / GIA-HAO
  [ICLR 2022] Understanding and Improving Graph Injection Attack by Promoting Unnoticeability
  ☆38Updated last year
• xiaozhanguva / Cost-Sensitive-Robustness
  A general method for training cost-sensitive robust classifier
  ☆22Updated 6 years ago
• ninghaohello / Interpretation2Adversary
  Adversarial learning by utilizing model interpretation
  ☆10Updated 6 years ago
• BUPT-GAMMA / LafAK
  The code of paper "Adversarial Label-Flipping Attack and Defense for Graph Neural Networks" (ICDM 2020)
  ☆18Updated 4 years ago
• PurdueMINDS / RelationalPooling
  ☆35Updated 6 years ago
• cf020031308 / LinkDist
  Distillation Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages.
  ☆15Updated 4 years ago
• weigq / neurips2021_stats
  ☆35Updated 3 years ago
• matenure / OTCoarsening
  Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
  ☆37Updated 4 years ago
• amiratag / neuronshapley
  Code for "Neuron Shapley: Discovering the Responsible Neurons"
  ☆26Updated last year