README.md

---
license: mit
datasets:
- ILSVRC/imagenet-1k
tags:
- uncertainty quantification
- model robustness
- selective classification
- label-smoothing
---

[![arXiv](https://img.shields.io/badge/arXiv-2403.14715-b31b1b.svg)](https://arxiv.org/abs/2403.14715)

This repository contains the models trained as experimental support for the paper "Towards Understanding Why Label Smoothing Degrades Selective Classification and How to Fix It" published at ICLR 2025.

The code is based on [TorchUncertainty](https://github.com/ENSTA-U2IS-AI/torch-uncertainty) and available on [GitHub](https://github.com/o-laurent/Label-smoothing-Selective-classification).

## List of models

This repository contains:
- for classification on ImageNet with ViTs: 4 ViTs-S/16 trained with label-smoothing coefficients in [0, 0.1, 0.2, 0.3]
- for classification on ImageNet with ResNets: 4 ResNet-50 trained with label-smoothing coefficients in [0, 0.1, 0.2, 0.3]
- for classification on CIFAR-100: 4 DenseNet-BC trained with label-smoothing coefficients in [0, 0.1, 0.2, 0.3]
- for segmentation: 4 DeepLabv3+ Resnet-101 trained with label-smoothing coefficients in [0, 0.1, 0.2, 0.3]
- for nlp: one CE-based and one LS-based (LS coefficient 0.6) LSTM-MLP

The rest of the models (notably on tabular data) used in the paper are trainable on CPU in the dedicated notebooks.