Awesome

Distance Aware Bottleneck

This repo contains the code for the paper A Rate-Distortion View of Uncertainty Quantification, which was presented at ICML 2024.

Citation

If you found our code or paper helpful, please cite it:

@inproceedings{dab_apostoletal,
title={A Rate-Distortion View of Uncertainty Quantification},
author={Apostolopoulou, Ifigeneia and Eysenbach, Benjamin and Nielsen, Frank and Dubrawski, Artur},
booktitle={International Conference on Machine Learning},
year={2024},
url={https://arxiv.org/abs/2406.10775}
}

Install

Cuda Envirnoment

Below, we provide the exact cuda environment for our tensorflow version:

   nvidia-cublas-cu11==11.11.3.6
   nvidia-cuda-cupti-cu11==11.8.87
   nvidia-cuda-nvcc-cu11==11.8.89
   nvidia-cuda-runtime-cu11==11.8.89
   nvidia-cudnn-cu11==8.6.0.163
   nvidia-cufft-cu11==10.9.0.58
   nvidia-curand-cu11==10.3.0.86
   nvidia-cusolver-cu11==11.4.1.48
   nvidia-cusparse-cu11==11.7.5.86

Dependecies

DAB is built in Python 3.9.18 using Tensorflow 2.12.0. The cuda version is 11.8. Use the following command to install the requirements:

pip install -r requirements.txt

Datasets

All datasets are placed in ~/tensorflow_datasets

• Download from Tensorflow Datasets for cifar10, cifar100, SVHN.
• Use cifar10_corrupted for the cifar10 noise corruptions.
• For preparing ImageNet for use with tensorflow_datasets, these instructions might be useful.
• For ImageNetO, please download the serialized dataset from here, and place it in the ~/tensorflow_datasets directory.

Train DAB

Synthetic Regression

python synthetic_regression_demo.py --example=1 --codebook_size=1

python synthetic_regression_demo.py --example=2 --codebook_size=2

Cifar10 (Tables 2, 3, 4 in the paper)

python run_cifar.py  --num_cores=4  --dab_dim=8 --codebook_size=10 --train_epochs=200 --seed=3

ImageNet-1K (Table 5 in the paper)

python run_imagenet.py --codebook_size=1000 --dab_dim=80 --num_cores=4 --per_core_batch_size=256 --base_learning_rate=0.1 --rdfc_learning_rate=0.1 --dab_tau=2.0 --beta=0.02 --calibrate=True --uncertainty_lb=100 --seed=243 

python run_imagenet.py --codebook_size=1000 --dab_dim=80 --num_cores=4 --per_core_batch_size=256 --base_learning_rate=0.04 --rdfc_learning_rate=0.1 --dab_tau=2.0 --beta=0.04 --calibrate=True --uncertainty_lb=100 --seed=243 --backpropagate=False 

python run_imagenet.py --codebook_size=1000 --dab_dim=80 --num_cores=4 --per_core_batch_size=256 --base_learning_rate=0.1 --rdfc_learning_rate=0.4 --dab_tau=2.0 --beta=0.01 --calibrate=False --seed=243 

python run_imagenet.py --codebook_size=1000 --dab_dim=80 --num_cores=4 --per_core_batch_size=256 --base_learning_rate=0.05 --rdfc_learning_rate=0.5 --dab_tau=2.0 --beta=0.005 --calibrate=False --seed=243 --backpropagate=False 

Evaluate DAB

Trained Models

You can download the trained DAB models presented in the paper here.

Cifar10

python run_cifar.py --num_cores=4 --dab_dim=8 --codebook_size=10 --dab_tau=1.0 --eval_only=True --saved_model_dir=<ABSOLUTE_PATH>/trained_models/cifar/ 

ImageNet-1K

python run_imagenet.py --codebook_size=1000 --dab_dim=80 --num_cores=4 --per_core_batch_size=256 --dab_tau=2.0 --eval_only=True --saved_model_dir=<ABSOLUTE_PATH>/trained_models/imagenet_finetuned_ood/ 

python run_imagenet.py --codebook_size=1000 --dab_dim=80 --num_cores=4 --per_core_batch_size=256 --dab_tau=2.0  --eval_only=True --saved_model_dir=<ABSOLUTE_PATH>/trained_models/imagenet_pretrained_ood/

python run_imagenet.py --codebook_size=1000 --dab_dim=80 --num_cores=4 --per_core_batch_size=256 --dab_tau=2.0 --eval_only=True --saved_model_dir=<ABSOLUTE_PATH>/trained_models/imagenet_finetuned_calibrated/ 

python run_imagenet.py --codebook_size=1000 --dab_dim=80 --num_cores=4 --per_core_batch_size=256 --dab_tau=2.0  --eval_only=True --saved_model_dir=<ABSOLUTE_PATH>/trained_models/imagenet_pretrained_calibrated/

Contact

For any questions or problems using the library, please contact me at ifiaposto@gmail.com.