fangliancheng / featurecp

The official codebase for Predictive Inference with Feature Conformal Prediction

FeatureCP

Predictive Inference with Feature Conformal Prediction (Accepted by ICLR 2023)

Jiaye Teng*, Chuan Wen*, Dinghuai Zhang*, Yoshua Bengio, Yang Gao, Yang Yuan

1. Installation

We use Python 3.7, and other packages can be installed by:

pip install -r requirements.txt

2. Run FeactureCP

2.1 One-dim datasets

The 1-dim datasets include Community, Facebook1, Facebook2, Meps19, Meps20, Meps21, Star, Blog, Bio and Bike, which have been placed in ./datasets/. The results of FeatureCP on these datasets can be obtained by the following commands:

python main.py --data com --no-resume --seed 0 1 2 3 4
python main.py --data fb1 --no-resume --seed 0 1 2 3 4
python main.py --data fb2 --no-resume --seed 0 1 2 3 4
python main.py --data meps19 --no-resume --seed 0 1 2 3 4
python main.py --data meps20 --no-resume --seed 0 1 2 3 4
python main.py --data meps21 --no-resume --seed 0 1 2 3 4
python main.py --data star --no-resume --seed 0 1 2 3 4
python main.py --data blog --no-resume --seed 0 1 2 3 4
python main.py --data bio --no-resume --feat_lr 0.001 --seed 0 1 2 3 4
python main.py --data bike --no-resume --feat_lr 0.001 --seed 0 1 2 3 4

2.2 High-dimensional datasets

Synthetic dataset:

python main.py --data x100-y10-reg --no-resume --feat_lr 0.001 --feat_step 80 --seed 0 1 2 3 4

Cityscapes

Cityscapes is a commonly-used semantic segmentation dataset. Please download it from the official website.

The FCN model need to firstly be trained on Cityscapes with the codebase in ./FCN-Trainer/. We also provide the checkpoint trained by us in this link. Please train the FCN to get the model checkpoint or simply download our checkpoint, and move it into ./ckpt/cityscapes/.

Note: As discussed in Section 5.1 and Appendix B.1 in our paper, we transform the original pixel-wise classification problem into a high-dimensional pixel-wise regression problem. Specifically, we convert the label space from $[0, 1]$ to $(−\infty, +\infty)$ by the double log trick, i.e., $\dot{y}=log(-log(\tilde{y}))$. Therefore, if you want to visualize the estimated length as Figure 3 or evaluate the mIoU of the provided FCN model, you need to convert the output space to the original label space by output = torch.exp(-torch.exp(output)).

The command to execute the Cityscapes experiment is:

export $CITYSCAPES_PATH = 'your path to the cityscapes'
python main_fcn.py --device 0 --data cityscapes --dataset-dir $CITYSCAPES_PATH --batch_size 20 --feat_step 10 --feat_lr 0.1 --workers 10 --seed 0 1 2 3 4

If you want to save the visualization of estimated length like Figure 3, you can add --visualize in the command.

3. Citation

If you find our work is helpful to you, please cite our paper:

@inproceedings{
teng2023predictive,
title={Predictive Inference with Feature Conformal Prediction},
author={Jiaye Teng and Chuan Wen and Dinghuai Zhang and Yoshua Bengio and Yang Gao and Yang Yuan},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023}
}