Out-of-distribution (OOD) detection is a critical requirement for the deployment of deep neural networks. This paper introduces the HEAT model, a new post-hoc OOD detection method estimating the density of in-distribution (ID) samples using hybrid energy-based models (EBM) in the feature space of a pre-trained backbone. HEAT complements prior density estimators of the ID density, e.g. parametric models like the Gaussian Mixture Model (GMM), to provide an accurate yet robust density estimation. A second contribution is to leverage the EBM framework to provide a unified density estimation and to compose several energy terms. Extensive experiments demonstrate the significance of the two contributions. HEAT sets new state-of-the-art OOD detection results on the CIFAR-10 / CIFAR100 benchmark as well as on the large-scale Imagenet benchmark. The code is available at: github.com/MarcLafon/heatood.
@article{Lafon2023,author={Lafon, Marc and Elias, Ramzi and Clément, Rambour and Thome, Nicolas},journal={Proceedings of the 40 th International Conference on Machine
Learning, Honolulu, Hawaii, USA. PMLR 202, 2023},title={{Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection}},year={2023},}
2022
NeurIPS
Energy Correction Model in the Feature Space for Out-of-Distribution Detection
In this work, we study the out-of-distribution (OOD) detection problem through the use of the feature space of a pre-trained deep classifier. We show that learning the density of in-distribution (ID) features with an energy-based models (EBM) leads to competitive detection results. However, we found that the non-mixing of MCMC sampling during the EBM’s training undermines its detection performance. To overcome this an energy-based correction of a mixture of class-conditional Gaussian distributions. We obtains favorable results when compared to a strong baseline like the KNN detector on the CIFAR-10/CIFAR-100 OOD detection benchmarks.
@article{Lafon2022,author={Lafon, Marc and Clément, Rambour and Thome, Nicolas},journal={NeurIPS Workshop on Machine Learning Safety},title={{Energy Correction Model in the Feature Space for Out-of-Distribution Detection}},year={2022},}
2021
ICML
Beyond First-Order Uncertainty Estimation with Evidential Models for Open-World Recognition
Charles Corbière, Marc Lafon, Nicolas Thome, and
2 more authors
ICML Workshop on Uncertainty and Robustness in Deep Learning, 2021
In this paper, we tackle the challenge of jointly quantifying in-distribution and out-of-distribution (OOD) uncertainties. We introduce KLoS, a KL-divergence measure defined on the class-probability simplex. By leveraging the second-order uncertainty representation provided by evi-dential models, KLoS captures more than existing first-order uncertainty measures such as predic-tive entropy. We design an auxiliary neural network , KLoSNet, to learn a refined measure directly aligned with the evidential training objective. Experiments show that KLoSNet acts as a class-wise density estimator and outperforms current uncertainty measures in the realistic context where no OOD data is available during training. We also report comparisons in the presence of OOD training samples, which shed a new light on the impact of the vicinity of this data with OOD test data.
@article{Corbiere2021,author={Corbière, Charles and Lafon, Marc and Thome, Nicolas and Cord, Matthieu and Pérez, Patrick},journal={ICML Workshop on Uncertainty and Robustness in Deep Learning},title={{Beyond First-Order Uncertainty Estimation with Evidential Models for Open-World Recognition}},year={2021},}
arxiv
Understanding the Double Descent Phenomenon in Deep Learning
Combining empirical risk minimization with capacity control is a classical strategy in machine learning when trying to control the generalization gap and avoid overfitting, as the model class capacity gets larger. Yet, in modern deep learning practice, very large over-parameterized models (e.g. neural networks) are optimized to fit perfectly the training data and still obtain great generalization performance. Past the interpolation point, increasing model complexity seems to actually lower the test error. In this tutorial, we explain the concept of double descent introduced by Belkin et al 2019, and its mechanisms. Section 1 sets the classical statistical learning framework and introduces the double descent phenomenon. By looking at a number of examples, section 2 introduces inductive biases that appear to have a key role in double descent by selecting, among the multiple interpolating solutions, a smooth empirical risk minimizer. Finally, section 3 explores the double descent with two linear models, and gives other points of view from recent related works.
@article{Lafon2021,author={Lafon, Marc and Thomas, Alexandre},journal={},title={{Understanding the Double Descent Phenomenon in Deep Learning}},year={2021},}