This is the code repository for the AAAI 2024 paper "Unknown-Aware Graph Regularization for Robust Semi-Supervised Learning from Uncurated Data" (PyTorch implementation).
This implementation is based on Pytorch-FixMatch.
This repository needs CIFAR-10, CIFAR-100, or ImageNet-30 to train a model.
To fully reproduce the results in evaluation, we also need SVHN, LSUN, ImageNet for CIFAR10, 100, and LSUN, DTD, CUB, Flowers, Caltech_256, Stanford Dogs for ImageNet-30. To prepare the datasets above, follow CSI, OpenMatch.
All datasets are supposed to be under ./data.
Train the model for CIFAR-10 dataset under correlated and uncorrelated settings:
sh run_cifar10_corr.sh (gpu_id) (num_labeled)
sh run_cifar10_uncorr.sh (gpu_id) (num_labeled)
Train the model for CIFAR-100 dataset under correlated and uncorrelated settings:
sh run_cifar100_corr.sh (gpu_id) (num_labeled)
sh run_cifar100_uncorr.sh (gpu_id) (num_labeled)
Run experiments on ImageNet-30:
sh run_imagenet.sh (gpu_id)