Mitigating Neural Network Overconfidence with Logit Normalization

ICML 2022: This repository is the official implementation of LogitNorm.

Requirements

To install requirements:

pip install -r requirements.txt

Training

To train the model(s) in the paper, run this command:

# CE loss
python train.py cifar10 --alg standard -m wrn --exp_name normal --gpu 7


# LogitNorm loss
python train.py cifar10 --alg standard -m wrn --exp_name logitnorm --gpu 7 --loss logit_norm --temp 0.01

Evaluation

To evaluate the model on CIFAR-10, run:

python test.py cifar10 --method_name cifar10_wrn_${exp_name}_standard --num_to_avg 10 --gpu 0 --seed 1 --prefetch 0

# Example with pretrained model
python test.py cifar10 --method_name cifar10_wrn_logitnorm_standard --num_to_avg 10 --gpu 0 --seed 1 --prefetch 0

What's More?

Below are my other research works related to this topic:

1. Using OOD examples to improve robustness against inherent noisy labels: NeurIPS 2021 | Code
2. Can we use OOD examples to rebalance long-tailed dataset? ICML 22 | Code

Citation

If you find this useful in your research, please consider citing:

@article{wei2022logitnorm,
title={Mitigating Neural Network Overconfidence with Logit Normalization},
author={Wei, Hongxin and Xie, Renchunzi and Cheng, Hao and Feng, Lei and An, Bo and Li, Yixuan},
booktitle={International Conference on Machine Learning (ICML)},
year={2022}
}