Row 7536

Row ID: 7536 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 7536 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

https://arxiv.org/abs/2405.08766

Out-of-distribution (OOD) detection is critical when deploying machine learning models in the real world. Outlier exposure methods, which incorporate auxiliary outlier data in the training process, can drastically improve OOD detection performance compared to approaches without advanced training strategies. We introduce Hopfield Boosting, a boosting approach, which leverages modern Hopfield energy (MHE) to sharpen the decision boundary between the in-distribution and OOD data. Hopfield Boosting encourages the model to concentrate on hard-to-distinguish auxiliary outlier examples that lie close to the decision boundary between in-distribution and auxiliary outlier data. Our method achieves a new state-of-the-art in OOD detection with outlier exposure, improving the FPR95 metric from 2.28 to 0.92 on CIFAR-10 and from 11.76 to 7.94 on CIFAR-100.

FieldValue
text https://arxiv.org/abs/2405.08766 Out-of-distribution (OOD) detection is critical when deploying machine learning models in the real world. Outlier exposure methods, which incorporate auxiliary outlier data in the training process, can drastically improve OOD detection performance compared to approaches without advanced training strategies. We introduce Hopfield Boosting, a boosting approach, which leverages modern Hopfield energy (MHE) to sharpen the decision boundary between the in-distributio…
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-16
username_encoded Z0FBQUFBQm5LakwzVFBUYkpNZHlON3ZDYTViTkxpY1FWQlptNm1XX1BScmxkQmFjY1JVYmVDYXhCZi1NMWFESUpMbmJTUC05VW15aENHQmxmVGRtM3p4ZFhiLWpZSGoybGc9PQ==
url_encoded Z0FBQUFBQm5Lak9IWVZDN2I1SDg3UnVHd21HNVRjS2FnTmlud0lTSm9YNzdGNzBfaWR5bXc0eHotaE9rYWVwbi1WNXRIcUl0eFByZGpBSFdXU01rWGUyS1l5NmxuMEN0RHRTeHFiZG9XZFRFNDJpc0Y1Uy1WOTd0Qk42SVcxc085dUZFVXlJNmZUQlJYT0R5YjZXbUxiMFBrZVdEc1pZdmEzVEVSMFNlSUcwRHhoN3N5Um5aaHRUTEtLZDJqLWRLbS0wN1ROaXFqTW0w

Raw Record

{
  "text": "https://arxiv.org/abs/2405.08766\n\nOut-of-distribution (OOD) detection is critical when deploying machine learning models in the real world. Outlier exposure methods, which incorporate auxiliary outlier data in the training process, can drastically improve OOD detection performance compared to approaches without advanced training strategies. We introduce Hopfield Boosting, a boosting approach, which leverages modern Hopfield energy (MHE) to sharpen the decision boundary between the in-distribution and OOD data. Hopfield Boosting encourages the model to concentrate on hard-to-distinguish auxiliary outlier examples that lie close to the decision boundary between in-distribution and auxiliary outlier data. Our method achieves a new state-of-the-art in OOD detection with outlier exposure, improving the FPR95 metric from 2.28 to 0.92 on CIFAR-10 and from 11.76 to 7.94 on CIFAR-100.",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-16",
  "username_encoded": "Z0FBQUFBQm5LakwzVFBUYkpNZHlON3ZDYTViTkxpY1FWQlptNm1XX1BScmxkQmFjY1JVYmVDYXhCZi1NMWFESUpMbmJTUC05VW15aENHQmxmVGRtM3p4ZFhiLWpZSGoybGc9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9IWVZDN2I1SDg3UnVHd21HNVRjS2FnTmlud0lTSm9YNzdGNzBfaWR5bXc0eHotaE9rYWVwbi1WNXRIcUl0eFByZGpBSFdXU01rWGUyS1l5NmxuMEN0RHRTeHFiZG9XZFRFNDJpc0Y1Uy1WOTd0Qk42SVcxc085dUZFVXlJNmZUQlJYT0R5YjZXbUxiMFBrZVdEc1pZdmEzVEVSMFNlSUcwRHhoN3N5Um5aaHRUTEtLZDJqLWRLbS0wN1ROaXFqTW0w"
}
Explore Dataset Explore Row

Entry Information