On the Importance of Regularisation & Auxiliary Information in OOD Detection

• URL: http://arxiv.org/abs/2107.07564v1
• Date: Thu, 15 Jul 2021 18:57:10 GMT
• Title: On the Importance of Regularisation & Auxiliary Information in OOD Detection
• Authors: John Mitros and Brian Mac Namee
• Abstract summary: This deficiency demonstrates a fundamental flaw indicating that neural networks often overfit on spurious correlations. We present two novel objectives that improve the ability of a network to detect out-of-distribution samples.
• Score: 9.340611077939828
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural networks are often utilised in critical domain applications (e.g.~self-driving cars, financial markets, and aerospace engineering), even though they exhibit overconfident predictions for ambiguous inputs. This deficiency demonstrates a fundamental flaw indicating that neural networks often overfit on spurious correlations. To address this problem in this work we present two novel objectives that improve the ability of a network to detect out-of-distribution samples and therefore avoid overconfident predictions for ambiguous inputs. We empirically demonstrate that our methods outperform the baseline and perform better than the majority of existing approaches, while performing competitively those that they don't outperform. Additionally, we empirically demonstrate the robustness of our approach against common corruptions and demonstrate the importance of regularisation and auxiliary information in out-of-distribution detection.

Related papers

• Deep Neural Networks Tend To Extrapolate Predictably [51.303814412294514]
  neural network predictions tend to be unpredictable and overconfident when faced with out-of-distribution (OOD) inputs. We observe that neural network predictions often tend towards a constant value as input data becomes increasingly OOD. We show how one can leverage our insights in practice to enable risk-sensitive decision-making in the presence of OOD inputs.
  arXiv  Detail & Related papers  (2023-10-02T03:25:32Z)
• DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications [54.93807822347193]
  We show how to adapt attribution robustness estimation methods to a given domain, so as to take into account domain-specific plausibility. Next, we provide two methods, adversarial training and FAR training, to mitigate the brittleness characterized by DARE. Finally, we empirically validate our methods with extensive experiments on three established biomedical benchmarks.
  arXiv  Detail & Related papers  (2023-07-05T08:11:40Z)
• Implicit Visual Bias Mitigation by Posterior Estimate Sharpening of a Bayesian Neural Network [7.488317734152586]
  We propose a novel implicit mitigation method using a Bayesian neural network. Our proposed posterior estimate sharpening procedure encourages the network to focus on core features that do not contribute to high uncertainties.
  arXiv  Detail & Related papers  (2023-03-29T09:47:35Z)
• Free Lunch for Generating Effective Outlier Supervision [46.37464572099351]
  We propose an ultra-effective method to generate near-realistic outlier supervision. Our proposed textttBayesAug significantly reduces the false positive rate over 12.50% compared with the previous schemes.
  arXiv  Detail & Related papers  (2023-01-17T01:46:45Z)
• Two-stage Modeling for Prediction with Confidence [0.0]
  It is difficult to generalize the performance of neural networks under the condition of distributional shift. We propose a novel two-stage model for the potential distribution shift problem. We show that our model offers reliable predictions for the vast majority of datasets.
  arXiv  Detail & Related papers  (2022-09-19T08:48:07Z)
• Towards Unbiased Visual Emotion Recognition via Causal Intervention [63.74095927462]
  We propose a novel Emotion Recognition Network (IERN) to alleviate the negative effects brought by the dataset bias. A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-07-26T10:40:59Z)
• Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation [74.05906222376608]
  We propose adversarial self-supervision UDA (or ASSUDA) that maximizes the agreement between clean images and their adversarial examples by a contrastive loss in the output space. This paper is rooted in two observations: (i) the robustness of UDA methods in semantic segmentation remains unexplored, which pose a security concern in this field; and (ii) although commonly used self-supervision (e.g., rotation and jigsaw) benefits image tasks such as classification and recognition, they fail to provide the critical supervision signals that could learn discriminative representation for segmentation tasks.
  arXiv  Detail & Related papers  (2021-05-23T01:50:44Z)
• Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks [15.217367754000913]
  It is increasingly important to obtain models with high robustness that are resistant to adversarial examples. We give preliminary definitions on what adversarial attacks and robustness are. We study frequently-used benchmarks and mention theoretically-proved bounds for adversarial robustness.
  arXiv  Detail & Related papers  (2020-11-03T07:42:53Z)
• Accurate and Robust Feature Importance Estimation under Distribution Shifts [49.58991359544005]
  PRoFILE is a novel feature importance estimation method. We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
  arXiv  Detail & Related papers  (2020-09-30T05:29:01Z)
• Vulnerability Under Adversarial Machine Learning: Bias or Variance? [77.30759061082085]
  We investigate the effect of adversarial machine learning on the bias and variance of a trained deep neural network. Our analysis sheds light on why the deep neural networks have poor performance under adversarial perturbation. We introduce a new adversarial machine learning algorithm with lower computational complexity than well-known adversarial machine learning strategies.
  arXiv  Detail & Related papers  (2020-08-01T00:58:54Z)

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.