Repository files navigation

This repo contains the official code forOn Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty.

It also contains an implementation ofSimple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness (or SNGP), for easy comparison.We only implement the exact predictive covariance version, which is both simpler and performs better than the momentum based scheme.

Make sure the dependencies listed inenvironment.yml are available and run:

python train_due.py

which will automatically download the dataset (CIFAR10 by default), and start training.There are several command line flags available for changing the hyper-parameters.

A model trained using the defaults is available fromhere.

A regression example is implemented intoy_regression.ipynb.

If you want to train SNGP, simply add the flag (and adjust the learning rate):

python train_due.py --sngp --learning_rate 0.05

The repository is split into a reusable library and utils only used for the specific training script. You can install the library part using pip:

pip install --upgrade git+https://github.com/y0ast/DUE.git

Alternatively you can just copy over the components you want!

If you use this repository, please cite:

@article{van2021on,  title={On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty},  author={van Amersfoort, Joost and Smith, Lewis and Jesson, Andrew and Key, Oscar and Gal, Yarin},  journal={arXiv preprint arXiv:2102.11409},  year={2021}}

If you use the SNGP model, then please cite the original paper:

@article{liu2020simple,  title={Simple and principled uncertainty estimation with deterministic deep learning via distance awareness},  author={Liu, Jeremiah and Lin, Zi and Padhy, Shreyas and Tran, Dustin and Bedrax Weiss, Tania and Lakshminarayanan, Balaji},  journal={Advances in Neural Information Processing Systems},  volume={33},  pages={7498--7512},  year={2020}}

Thanks toLewis for the RFF implementation,John for testing DUE,Jishnu for evaluating SNGP, andJeremiah for checking SNGP.