Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018

Last update: Nov 29, 2022

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Overview

Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples

This project is for the paper "Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples". Some codes are from odin-pytorch.

Preliminaries

It is tested under Ubuntu Linux 16.04.1 and Python 2.7 environment, and requries Pytorch package to be installed:

Pytorch: Only GPU version is available.

Downloading Out-of-Distribtion Datasets

We use download links of two out-of-distributin datasets from odin-pytorch:

Tiny-ImageNet (resize)
LSUN (resize)

Training scripts

run_cross_entropy.sh: train the models using standard cross entropy loss.
run_joint_confidence.sh: train the models using joint confidence loss.

Test scripts

test.sh --dataset --out_dataset --pre_trained_net
--dataset = name of in-distribution (svhn or cifar10)
--out_dataset = name of out-of-distribution (svhn, cifar10, lsun or imagenet)
--pre_trained_net = path to pre_trained_net

Owner

GitHub Repository

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