Soft Labeling Affects Out-of-Distribution Detection of Deep Neural
Networks
- URL: http://arxiv.org/abs/2007.03212v1
- Date: Tue, 7 Jul 2020 05:50:52 GMT
- Title: Soft Labeling Affects Out-of-Distribution Detection of Deep Neural
Networks
- Authors: Doyup Lee and Yeongjae Cheon
- Abstract summary: We show that soft labeling can determine OOD detection performance.
How to regularize outputs of incorrect classes by soft labeling can deteriorate or improve OOD detection.
- Score: 4.56877715768796
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Soft labeling becomes a common output regularization for generalization and
model compression of deep neural networks. However, the effect of soft labeling
on out-of-distribution (OOD) detection, which is an important topic of machine
learning safety, is not explored. In this study, we show that soft labeling can
determine OOD detection performance. Specifically, how to regularize outputs of
incorrect classes by soft labeling can deteriorate or improve OOD detection.
Based on the empirical results, we postulate a future work for OOD-robust DNNs:
a proper output regularization by soft labeling can construct OOD-robust DNNs
without additional training of OOD samples or modifying the models, while
improving classification accuracy.
Related papers
- How Does Unlabeled Data Provably Help Out-of-Distribution Detection? [63.41681272937562]
Unlabeled in-the-wild data is non-trivial due to the heterogeneity of both in-distribution (ID) and out-of-distribution (OOD) data.
This paper introduces a new learning framework SAL (Separate And Learn) that offers both strong theoretical guarantees and empirical effectiveness.
arXiv Detail & Related papers (2024-02-05T20:36:33Z) - Energy-based Out-of-Distribution Detection for Graph Neural Networks [76.0242218180483]
We propose a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe.
GNNSafe achieves up to $17.0%$ AUROC improvement over state-of-the-arts and it could serve as simple yet strong baselines in such an under-developed area.
arXiv Detail & Related papers (2023-02-06T16:38:43Z) - Harnessing Out-Of-Distribution Examples via Augmenting Content and Style [93.21258201360484]
Machine learning models are vulnerable to Out-Of-Distribution (OOD) examples.
This paper proposes a HOOD method that can leverage the content and style from each image instance to identify benign and malign OOD data.
Thanks to the proposed novel disentanglement and data augmentation techniques, HOOD can effectively deal with OOD examples in unknown and open environments.
arXiv Detail & Related papers (2022-07-07T08:48:59Z) - On the Impact of Spurious Correlation for Out-of-distribution Detection [14.186776881154127]
We present a new formalization and model the data shifts by taking into account both the invariant and environmental features.
Our results suggest that the detection performance is severely worsened when the correlation between spurious features and labels is increased in the training set.
arXiv Detail & Related papers (2021-09-12T23:58:17Z) - Provably Robust Detection of Out-of-distribution Data (almost) for free [124.14121487542613]
Deep neural networks are known to produce highly overconfident predictions on out-of-distribution (OOD) data.
In this paper we propose a novel method where from first principles we combine a certifiable OOD detector with a standard classifier into an OOD aware classifier.
In this way we achieve the best of two worlds: certifiably adversarially robust OOD detection, even for OOD samples close to the in-distribution, without loss in prediction accuracy and close to state-of-the-art OOD detection performance for non-manipulated OOD data.
arXiv Detail & Related papers (2021-06-08T11:40:49Z) - Learn what you can't learn: Regularized Ensembles for Transductive
Out-of-distribution Detection [76.39067237772286]
We show that current out-of-distribution (OOD) detection algorithms for neural networks produce unsatisfactory results in a variety of OOD detection scenarios.
This paper studies how such "hard" OOD scenarios can benefit from adjusting the detection method after observing a batch of the test data.
We propose a novel method that uses an artificial labeling scheme for the test data and regularization to obtain ensembles of models that produce contradictory predictions only on the OOD samples in a test batch.
arXiv Detail & Related papers (2020-12-10T16:55:13Z) - Robust Out-of-distribution Detection for Neural Networks [51.19164318924997]
We show that existing detection mechanisms can be extremely brittle when evaluating on in-distribution and OOD inputs.
We propose an effective algorithm called ALOE, which performs robust training by exposing the model to both adversarially crafted inlier and outlier examples.
arXiv Detail & Related papers (2020-03-21T17:46:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.