Contrastive Normalizing Flow: Robust Uncertainty Estimation Under Distributional Shifts

This repository provides the official PyTorch implementation code of the paper.

Juhong Song, Jaewoong Choi, Myungjoo Kang, Contrastive Normalizing Flow: Robust Uncertainty Estimation Under Distributional Shifts

Dependencies

• PyTorch 1.7
• TensorFlow 2.3

Data

The base dataset of the experiments use MNIST and CIFAR.

For distributional shift datasets, MNIST rotation and CIFAR-10-C are used.

For OOD shift datasets, SVHN, LSUN, TinyImageNet, F-MNIST, NotMNIST, and EMNIST-letters are used.

Train the proposed model

Train CTNF for CIFAR dataset

python cifar_train.py --train_data_path <path> --c_data_path <Corruption data path> -- ood_data_path <OOD data path>

Train CTNF for MNIST dataset

python mnist_train.py --train_data_path <path> --c_data_path <Corruption data path> -- ood_data_path <OOD data path>

Experiment using pre-trained model

Assume that the pre-trained model is downloaded on ./data/weights folder.

CIFAR-10-Corruption and OOD Detection for SVHN, LSUN, and TinyImageNet

python experiments/cifar_c_main.py --ood svhn --valid_data_path <valid data path> --c_data_path <Corruption data path> --ood_path <your_path>

MNIST Rotation and OOD detection for F-MNIST, NotMNIST, and EMNIST-letters

python experiments/mnist_r_main.py --ood fmnist --valid_data_path <valid data path> --ood_path <your_path>