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

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Deep Learning Confident_classifier
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:

Training scripts

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
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Comments
  • PyTorch v1.x compatibility

    PyTorch v1.x compatibility

    On one hand this removes the now deprecated function load_lua which wasn't used in the source anyway. On the other it accesses the loss via loss.data.item() instead of loss.data[0] to conform to PyTorch v1.x semantics.

    Would you be willing to merge this PR? @pokaxpoka

    opened by flxai 0
  • Why the KL Divergence Loss needs to be multiplied by number of classes?

    Why the KL Divergence Loss needs to be multiplied by number of classes?

    https://github.com/alinlab/Confident_classifier/blob/462db01967f8a96374f2ab6a534b7c81fd872d2f/src/run_joint_confidence.py#L156 I'm bit confused about why you needa multiply the KL divergence loss by the number of classes. I can't find it from the definition of your paper, could you briefly explain it?

    opened by tim5go 0
  • Cannot reproduce figures from paper

    Cannot reproduce figures from paper

    I tried to use the source code from this repository and variations, but could not reproduce the figures seen in the paper. Could you please help me with directions to reproduce Figure 3.b and Figure 3.d as shown below?

    image

    What is the command to be used with the given source code to generate the samples seen above?

    opened by flxai 1
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