# Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks

@inproceedings{Sharma2021SketchingCF, title={Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks}, author={Apoorva Sharma and Navid Azizan and Marco Pavone}, booktitle={UAI}, year={2021} }

In order to safely deploy Deep Neural Networks (DNNs) within the perception pipelines of real-time decision making systems, there is a need for safeguards that can detect out-of-training-distribution (OoD) inputs both efﬁciently and ac-curately. Building on recent work leveraging the local curvature of DNNs to reason about epistemic uncertainty, we propose Sketching Curvature for OoD Detection (SCOD) , an architecture-agnostic framework for equipping any trained DNN with a task-relevant…

## 12 Citations

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## References

SHOWING 1-10 OF 41 REFERENCES

LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

- Computer ScienceArXiv
- 2015

This work proposes to amplify human effort through a partially automated labeling scheme, leveraging deep learning with humans in the loop, and constructs a new image dataset, LSUN, which contains around one million labeled images for each of 10 scene categories and 20 object categories.

A Scalable Laplace Approximation for Neural Networks

- Computer ScienceICLR
- 2018

This work uses recent insights from second-order optimisation for neural networks to construct a Kronecker factored Laplace approximation to the posterior over the weights of a trained network, enabling practitioners to estimate the uncertainty of models currently used in production without having to retrain them.

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

- Computer ScienceNIPS
- 2017

This work proposes an alternative to Bayesian NNs that is simple to implement, readily parallelizable, requires very little hyperparameter tuning, and yields high quality predictive uncertainty estimates.

Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation

- Computer ScienceICML
- 2019

It is proved that, for a classification problem with a large number of classes, if an input has a high confidence classification score, the inclusion of the Hessian term has small impacts in the final solution, and the empirical results indicate that considering group features can improve deep learning interpretation significantly.

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

- Computer ScienceICML
- 2016

A new theoretical framework is developed casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian processes, which mitigates the problem of representing uncertainty in deep learning without sacrificing either computational complexity or test accuracy.

Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks

- Computer ScienceICLR
- 2018

The proposed ODIN method, based on the observation that using temperature scaling and adding small perturbations to the input can separate the softmax score distributions between in- and out-of-distribution images, allowing for more effective detection, consistently outperforms the baseline approach by a large margin.

Orthogonal Gradient Descent for Continual Learning

- Computer ScienceAISTATS
- 2020

The Orthogonal Gradient Descent (OGD) method is presented, which accomplishes this goal by projecting the gradients from new tasks onto a subspace in which the neural network output on previous task does not change and the projected gradient is still in a useful direction for learning the new task.

Learning Multiple Layers of Features from Tiny Images

- Computer Science
- 2009

It is shown how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex, using a novel parallelization algorithm to distribute the work among multiple machines connected on a network.

Detecting Extrapolation with Local Ensembles

- Computer ScienceICLR
- 2020

This work focuses on underdetermination as a key component of extrapolation: it aims to detect when many possible predictions are consistent with the training data and model class, and uses local second-order information to approximate the variance of predictions across an ensemble of models from the same class.

Convolutional Networks with Dense Connectivity

- Computer ScienceIEEE transactions on pattern analysis and machine intelligence
- 2019

The Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion, and has several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially improve parameter efficiency.