Deep Deterministic Uncertainty

This repository contains the code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty.

If the code or the paper has been useful in your research, please add a citation to our work:

@article{mukhoti2021deterministic,
  title={Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty},
  author={Mukhoti, Jishnu and Kirsch, Andreas and van Amersfoort, Joost and Torr, Philip HS and Gal, Yarin},
  journal={arXiv preprint arXiv:2102.11582},
  year={2021}
}

Dependencies

The code is based on PyTorch and requires a few further dependencies, listed in environment.yml. It should work with newer versions as well.

OoD Detection

Datasets

For OoD detection, you can train on CIFAR-10/100. You can also train on Dirty-MNIST by downloading Ambiguous-MNIST (amnist_labels.pt and amnist_samples.pt) from here and using the following training instructions.

Training

In order to train a model for the OoD detection task, use the train.py script. Following are the main parameters for training:

--seed: seed for initialization
--dataset: dataset used for training (cifar10/cifar100/dirty_mnist)
--dataset-root: /path/to/amnist_labels.pt and amnist_samples.pt/ (if training on dirty-mnist)
--model: model to train (wide_resnet/vgg16/resnet18/resnet50/lenet)
-sn: whether to use spectral normalization (available for wide_resnet, vgg16 and resnets)
--coeff: Coefficient for spectral normalization
-mod: whether to use architectural modifications (leaky ReLU + average pooling in skip connections)
--save-path: path/for/saving/model/

As an example, in order to train a Wide-ResNet-28-10 with spectral normalization and architectural modifications on CIFAR-10, use the following:

python train.py \
       --seed 1 \
       --dataset cifar10 \
       --model wide_resnet \
       -sn -mod \
       --coeff 3.0

Similarly, to train a ResNet-18 with spectral normalization on Dirty-MNIST, use:

python train.py \
       --seed 1 \
       --dataset dirty-mnist \
       --dataset-root /home/user/amnist/ \
       --model resnet18 \
       -sn \
       --coeff 3.0

Evaluation

To evaluate trained models, use evaluate.py. This script can evaluate and aggregate results over multiple experimental runs. For example, if the pretrained models are stored in a directory path /home/user/models, store them using the following directory structure:

models
├── Run1
│   └── wide_resnet_1_350.model
├── Run2
│   └── wide_resnet_2_350.model
├── Run3
│   └── wide_resnet_3_350.model
├── Run4
│   └── wide_resnet_4_350.model
└── Run5
    └── wide_resnet_5_350.model

For an ensemble of models, store the models using the following directory structure:

model_ensemble
├── Run1
│   ├── wide_resnet_1_350.model
│   ├── wide_resnet_2_350.model
│   ├── wide_resnet_3_350.model
│   ├── wide_resnet_4_350.model
│   └── wide_resnet_5_350.model
├── Run2
│   ├── wide_resnet_10_350.model
│   ├── wide_resnet_6_350.model
│   ├── wide_resnet_7_350.model
│   ├── wide_resnet_8_350.model
│   └── wide_resnet_9_350.model
├── Run3
│   ├── wide_resnet_11_350.model
│   ├── wide_resnet_12_350.model
│   ├── wide_resnet_13_350.model
│   ├── wide_resnet_14_350.model
│   └── wide_resnet_15_350.model
├── Run4
│   ├── wide_resnet_16_350.model
│   ├── wide_resnet_17_350.model
│   ├── wide_resnet_18_350.model
│   ├── wide_resnet_19_350.model
│   └── wide_resnet_20_350.model
└── Run5
    ├── wide_resnet_21_350.model
    ├── wide_resnet_22_350.model
    ├── wide_resnet_23_350.model
    ├── wide_resnet_24_350.model
    └── wide_resnet_25_350.model

Following are the main parameters for evaluation:

--seed: seed used for initializing the first trained model
--dataset: dataset used for training (cifar10/cifar100)
--ood_dataset: OoD dataset to compute AUROC
--load-path: /path/to/pretrained/models/
--model: model architecture to load (wide_resnet/vgg16)
--runs: number of experimental runs
-sn: whether the model was trained using spectral normalization
--coeff: Coefficient for spectral normalization
-mod: whether the model was trained using architectural modifications
--ensemble: number of models in the ensemble
--model-type: type of model to load for evaluation (softmax/ensemble/gmm)

As an example, in order to evaluate a Wide-ResNet-28-10 with spectral normalization and architectural modifications on CIFAR-10 with OoD dataset as SVHN, use the following:

python evaluate.py \
       --seed 1 \
       --dataset cifar10 \
       --ood_dataset svhn \
       --load-path /path/to/pretrained/models/ \
       --model wide_resnet \
       --runs 5 \
       -sn -mod \
       --coeff 3.0 \
       --model-type softmax

Similarly, to evaluate the above model using feature density, set --model-type gmm. The evaluation script assumes that the seeds of models trained in consecutive runs differ by 1. The script stores the results in a json file with the following structure:

{
    "mean": {
        "accuracy": mean accuracy,
        "ece": mean ECE,
        "m1_auroc": mean AUROC using log density / MI for ensembles,
        "m1_auprc": mean AUPRC using log density / MI for ensembles,
        "m2_auroc": mean AUROC using entropy / PE for ensembles,
        "m2_auprc": mean AUPRC using entropy / PE for ensembles,
        "t_ece": mean ECE (post temp scaling)
        "t_m1_auroc": mean AUROC using log density / MI for ensembles (post temp scaling),
        "t_m1_auprc": mean AUPRC using log density / MI for ensembles (post temp scaling),
        "t_m2_auroc": mean AUROC using entropy / PE for ensembles (post temp scaling),
        "t_m2_auprc": mean AUPRC using entropy / PE for ensembles (post temp scaling)
    },
    "std": {
        "accuracy": std error accuracy,
        "ece": std error ECE,
        "m1_auroc": std error AUROC using log density / MI for ensembles,
        "m1_auprc": std error AUPRC using log density / MI for ensembles,
        "m2_auroc": std error AUROC using entropy / PE for ensembles,
        "m2_auprc": std error AUPRC using entropy / PE for ensembles,
        "t_ece": std error ECE (post temp scaling),
        "t_m1_auroc": std error AUROC using log density / MI for ensembles (post temp scaling),
        "t_m1_auprc": std error AUPRC using log density / MI for ensembles (post temp scaling),
        "t_m2_auroc": std error AUROC using entropy / PE for ensembles (post temp scaling),
        "t_m2_auprc": std error AUPRC using entropy / PE for ensembles (post temp scaling)
    },
    "values": {
        "accuracy": accuracy list,
        "ece": ece list,
        "m1_auroc": AUROC list using log density / MI for ensembles,
        "m2_auroc": AUROC list using entropy / PE for ensembles,
        "t_ece": ece list (post temp scaling),
        "t_m1_auroc": AUROC list using log density / MI for ensembles (post temp scaling),
        "t_m1_auprc": AUPRC list using log density / MI for ensembles (post temp scaling),
        "t_m2_auroc": AUROC list using entropy / PE for ensembles (post temp scaling),
        "t_m2_auprc": AUPRC list using entropy / PE for ensembles (post temp scaling)
    },
    "info": {dictionary of args}
}

Results

Dirty-MNIST

To visualise DDU's performance on Dirty-MNIST (i.e., Fig. 1 of the paper), use fig_1_plot.ipynb. The notebook requires a pretrained LeNet, VGG-16 and ResNet-18 with spectral normalization trained on Dirty-MNIST and visualises the softmax entropy and feature density for Dirty-MNIST (iD) samples vs Fashion-MNIST (OoD) samples. The notebook also visualises the softmax entropies of MNIST vs Ambiguous-MNIST samples for the ResNet-18+SN model (Fig. 2 of the paper). The following figure shows the output of the notebook for the LeNet, VGG-16 and ResNet18+SN model we trained on Dirty-MNIST.

CIFAR-10 vs SVHN

The following table presents results for a Wide-ResNet-28-10 architecture trained on CIFAR-10 with SVHN as the OoD dataset. For the full set of results, refer to the paper.

Method	Aleatoric Uncertainty	Epistemic Uncertainty	Test Accuracy	Test ECE	AUROC
Softmax	Softmax Entropy	Softmax Entropy	95.98+-0.02	0.85+-0.02	94.44+-0.43
Energy-based	Softmax Entropy	Softmax Density	95.98+-0.02	0.85+-0.02	94.56+-0.51
5-Ensemble	Predictive Entropy	Predictive Entropy	96.59+-0.02	0.76+-0.03	97.73+-0.31
DDU (ours)	Softmax Entropy	GMM Density	95.97+-0.03	0.85+-0.04	98.09+-0.10

Active Learning

To run active learning experiments, use active_learning_script.py. You can run active learning experiments on both MNIST as well as Dirty-MNIST. When running with Dirty-MNIST, you will need to provide a pretrained model on Dirty-MNIST to distinguish between clean MNIST and Ambiguous-MNIST samples. The following are the main command line arguments for active_learning_script.py.

--seed: seed used for initializing the first model (later experimental runs will have seeds incremented by 1)
--model: model architecture to train (resnet18)
-ambiguous: whether to use ambiguous MNIST during training. If this is set to True, the models will be trained on Dirty-MNIST, otherwise they will train on MNIST.
--dataset-root: /path/to/amnist_labels.pt and amnist_samples.pt/
--trained-model: model architecture of pretrained model to distinguish clean and ambiguous MNIST samples
-tsn: if pretrained model has been trained using spectral normalization
--tcoeff: coefficient of spectral normalization used on pretrained model
-tmod: if pretrained model has been trained using architectural modifications (leaky ReLU and average pooling on skip connections)
--saved-model-path: /path/to/saved/pretrained/model/
--saved-model-name: name of the saved pretrained model file
--threshold: Threshold of softmax entropy to decide if a sample is ambiguous (samples having higher softmax entropy than threshold will be considered ambiguous)
--subsample: number of clean MNIST samples to use to subsample clean MNIST
-sn: whether to use spectral normalization during training
--coeff: coefficient of spectral normalization during training
-mod: whether to use architectural modifications (leaky ReLU and average pooling on skip connections) during training
--al-type: type of active learning acquisition model (softmax/ensemble/gmm)
-mi: whether to use mutual information for ensemble al-type
--num-initial-samples: number of initial samples in the training set
--max-training-samples: maximum number of training samples
--acquisition-batch-size: batch size for each acquisition step

As an example, to run the active learning experiment on MNIST using the DDU method, use:

python active_learning_script.py \
       --seed 1 \
       --model resnet18 \
       -sn -mod \
       --al-type gmm

Similarly, to run the active learning experiment on Dirty-MNIST using the DDU baseline, with a pretrained ResNet-18 with SN to distinguish clean and ambiguous MNIST samples, use the following:

python active_learning_script.py \
       --seed 1 \
       --model resnet18 \
       -sn -mod \
       -ambiguous \
       --dataset-root /home/user/amnist/ \
       --trained-model resnet18 \
       -tsn \
       --saved-model-path /path/to/pretrained/model \
       --saved-model-name resnet18_sn_3.0_1_350.model \
       --threshold 1.0 \
       --subsample 1000 \
       --al-type gmm

Results

The active learning script stores all results in json files. The MNIST test set accuracy is stored in a json file with the following structure:

{
    "experiment run": list of MNIST test set accuracies one per acquisition step
}

When using ambiguous samples in the pool set, the script also stores the fraction of ambiguous samples acquired in each step in the following json:

{
    "experiment run": list of fractions of ambiguous samples in the acquired training set
}

Visualisation

To visualise results from the above json files, use the al_plot.ipynb notebook. The following diagram shows the performance of different baselines (softmax, ensemble PE, ensemble MI and DDU) on MNIST and Dirty-MNIST.

Questions

For any questions, please feel free to raise an issue or email us directly. Our emails can be found on the paper.

Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty

Related tags

Overview

Deep Deterministic Uncertainty

Dependencies

OoD Detection

Datasets

Training

Evaluation

Results

Dirty-MNIST

CIFAR-10 vs SVHN

Active Learning

Results

Visualisation

Questions

Owner

Jishnu Mukhoti

TSIT: A Simple and Versatile Framework for Image-to-Image Translation

Proposed n-stage Latent Dirichlet Allocation method - A Novel Approach for LDA

PyTorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision.

Fast and accurate optimisation for registration with little learningconvexadam

Implementation of ICCV2021(Oral) paper - VMNet: Voxel-Mesh Network for Geodesic-aware 3D Semantic Segmentation

NasirKhusraw - The TSP solved using genetic algorithm and show TSP path overlaid on a map of the Iran provinces & their capitals.

PyTorch-LIT is the Lite Inference Toolkit (LIT) for PyTorch which focuses on easy and fast inference of large models on end-devices.

[CVPR21] LightTrack: Finding Lightweight Neural Network for Object Tracking via One-Shot Architecture Search

PyTorch code for the paper "Curriculum Graph Co-Teaching for Multi-target Domain Adaptation" (CVPR2021)

Meli Data Challenge 2021 - First Place Solution

Lightweight Cuda Renderer with Python Wrapper.

Codebase for ECCV18 "The Sound of Pixels"

Fantasy Points Prediction and Dream Team Formation

ALFRED - A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

This project generates news headlines using a Long Short-Term Memory (LSTM) neural network.

Build upon neural radiance fields to create a scene-specific implicit 3D semantic representation, Semantic-NeRF

7th place solution of Human Protein Atlas - Single Cell Classification on Kaggle

[AAAI2022] Source code for our paper《Suppressing Static Visual Cues via Normalizing Flows for Self-Supervised Video Representation Learning》

a general-purpose Transformer based vision backbone

Localization Distillation for Object Detection