relu_networks_overconfident

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Why ReLU networks yield high-confidence predictions far away from the training data and how to mitig...

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Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem

CVPR 2019 (accepted with an oral)

Matthias Hein, Maksym Andriushchenko, Julian Bitterwolf

University of Tübingen and Saarland University

https://arxiv.org/abs/1812.05720

Main idea

Problem of overconfident predictions

ReLU-networks yield overconfident predictions on unrelated tasks. Here a ResNet-18 was trained on CIFAR-10 and evaluated on SVHN perceives digits as dogs, birds or airplanes with 100% confidence. Clearly, this questions the usage of neural networks for safety-critical tasks.

We can clearly see this on the two moons dataset: the classifier outputs 100% confidence almost everywhere, no matter how far away the points are from the training data.

Theoretical argument

We give a theoretical argument of why ReLU activation function can lead to models with overconfident predictions far away from the training data. We leverage the fact that a ReLU network partitions the input space on the finite set of polytopes.

The intuition is made formal in the main theorem.

New training methods to mitigate the problem

In order to mitigate this problem, we propose a training scheme CEDA that enforces uniform confidences on out-of-distribution noise shown below.

We can see that CEDA already improves on the problem by preventing overconfident predictions.

Next we propose a robust optimization training scheme ACET which minimizes the worst-case noise in a neighbourhood of noise points. This works even better.

Although, as predicted by Theorem 1, overconfident predictions still exist since this is an inherent property of ReLU-networks regardless of the training procedure. If we zoom out from 0, 1 to [-10, 10], we can clearly see that overconfident predictions are still there.

We provide a systematic comparison of ACET over plain and CEDA models on various benchmarks: from evaluation on noise to out-of-distribution detection on other image datasets.

We also illustrate the advantage of ACET on per-image basis. For example, if we train a plain model on MNIST and evaluate it on CIFAR-10, it is also prone to overconfident predictions (up to 99.6%) on images that do not have anything in common with digits.

However, ACET mitigates this problem to a large extent.

Experimental confirmation of Theorem 3.1

Finally, we illustrate Theorem 3.1 experimentally. We observe that simple alpha-scaling (i.e. brightness increase) of, e.g., uniform noise can easily lead to overconfident predictions for plain models even in the image domain [0, 1]^d. At the same time, ACET models helps to mitigate this problem as well.

Code for training ACET models

toy2d.ipynb
is a very short notebook that shows how to train an ACET model on two moons dataset. For full-fledged code on how to train CNNs with ACET on multiple GPUs with tensorboard summaries, please take a look onto
train.py
.

Training details

Models are created and trained by using the

train.py
script. Get the list of possible arguments via
python3 train.py --help
.

The value of

at_frac
determines the number of noise examples (on which the maximal confidence should be small) per clean training example. Thus, if
--at_frac=0
, a plain model will be trained. For the CEDA and ACET models we report in the paper, we set
--at_frac=1
, which means 50% clean and 50% (adversarial) noise images.

The value

pgd_niter
determines how many steps of pgd will be applied to the noise. If its value is zero, the first random step will not be applied and just the noise will be used -- this is the correct setting for CEDA training.

For ACET, the parameters

at_frac
,
pgd_niter
and
pgd_eps
should be positive. In the paper, we used
pgd_niter=40
and
pgd_eps=0.3
. While for plain and CEDA training the value of
pgd_eps
is not used, you can set it to compare their TensorBoard evaluations. As an example, for training the ACET model on SVHN we used the following command:

python3 train.py --exp_name='acet_svhn' --gpus=0 --n_epochs=100 --dataset=svhn --model=resnet_small --loss='max_conf' --opt=momentum --lr=0.1 --lmbd=0.0005 --pgd_eps=0.3 --at_frac=1 --pgd_niter=40

Pre-trained models from the paper

You can find all the models presented in Table 1 in the folder

exps_paper
. The file names of the models contain the hyperparameters used for their training. For example:
2019-04-04 16:27:49_dataset=cifar100 model=resnet_small p_norm=inf lmbd=0.0005 at_frac=1.0 pgd_eps=0.3 pgd_niter=40 frac_perm=0.5 loss=max_conf

means that the model was trained on CIFAR-100, the architecture was

resnet_small
(see
models.ResNetSmall
for its definition), ACET was applied on 50% examples in every batch (
at_frac=1.0
) wrt the Linf-norm
eps=0.3
, where the 40 steps of PGD were used in the robust optimization procedure. The loss function used in PGD was the maximum log probability taken over all classes.
frac_perm=0.5
means that the noise is generated with 50% uniform and 50% permutation noise.

Evaluation

Evaluation is split into two parts: Collecting the model outputs on several datasets, including adversarial noise and adversarial samples, and analyzing the output numbers.

First, run

evaluation.py
with the chosen hyperparameter settings for the evaluation as well as the model folder and name as arguments. This will save the predictions in numpy files within the model folder. For calculating the resulting statistics and plotting histograms and ROC curves, call
analysis.py
on that same folder. The resulting numbers are appended to a specified
.csv
file in the main folder, while you can find the plots as
.png
files within the model folder.

python3 evaluation.py --model_folder "{model_folder}" --model_name "{model_name}" --model "{model_type}" --dataset "{model_set} --gpus=1 --at_frac=1 --pgd_niter=80 --pgd_eps "{eps}" --pgd_step "{step}" --loss='max_conf'

python3 analysis.py --model_folder "{model_folder}" --save_all_evals='results.csv'

TensorBoard

During training, several metrics are recorded as TensorBoard logs, which can be reviewed with

tensorboard --logdir=exps
.

Requirements

Our code runs on

Python3.5
. For the computations we use
tensorflow:1.10.1
and dataloading is running with PyTorch version
0.4.1
and
torchvision
.

With Python3.5 installed, you can create a docker container that has all necessary dependencies installed by using the provided Dockerfile.

Contact

Please contact Maksym Andriushchenko or Julian Bitterwolf regarding this code.

Citation

@article{hein2019relu,
  title={Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem},
  author={Hein, Matthias and Andriushchenko, Maksym and Bitterwolf, Julian},
  conference={CVPR 2019},
  year={2019}
}

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