Adaptive Label Smoothing for Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2410.06134v1
• Date: Tue, 8 Oct 2024 15:35:11 GMT
• Title: Adaptive Label Smoothing for Out-of-Distribution Detection
• Authors: Mingle Xu, Jaehwan Lee, Sook Yoon, Dong Sun Park,
• Abstract summary: We propose a novel regularization method called adaptive label smoothing (ALS) ALS pushes the non-true classes to have same probabilities whereas the maximal probability is neither fixed nor limited. Our code will be available to the public.
• Score: 1.5999407512883508
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Out-of-distribution (OOD) detection, which aims to distinguish unknown classes from known classes, has received increasing attention recently. A main challenge within is the unavailable of samples from the unknown classes in the training process, and an effective strategy is to improve the performance for known classes. Using beneficial strategies such as data augmentation and longer training is thus a way to improve OOD detection. However, label smoothing, an effective method for classifying known classes, degrades the performance of OOD detection, and this phenomenon is under exploration. In this paper, we first analyze that the limited and predefined learning target in label smoothing results in the smaller maximal probability and logit, which further leads to worse OOD detection performance. To mitigate this issue, we then propose a novel regularization method, called adaptive label smoothing (ALS), and the core is to push the non-true classes to have same probabilities whereas the maximal probability is neither fixed nor limited. Extensive experimental results in six datasets with two backbones suggest that ALS contributes to classifying known samples and discerning unknown samples with clear margins. Our code will be available to the public.

Related papers

• Collaborative Feature-Logits Contrastive Learning for Open-Set Semi-Supervised Object Detection [75.02249869573994]
  In open-set scenarios, the unlabeled dataset contains both in-distribution (ID) classes and out-of-distribution (OOD) classes. Applying semi-supervised detectors in such settings can lead to misclassifying OOD class as ID classes. We propose a simple yet effective method, termed Collaborative Feature-Logits Detector (CFL-Detector)
  arXiv  Detail & Related papers  (2024-11-20T02:57:35Z)
• Margin-bounded Confidence Scores for Out-of-Distribution Detection [2.373572816573706]
  We propose a novel method called Margin bounded Confidence Scores (MaCS) to address the nontrivial OOD detection problem. MaCS enlarges the disparity between ID and OOD scores, which in turn makes the decision boundary more compact. Experiments on various benchmark datasets for image classification tasks demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2024-09-22T05:40:25Z)
• A noisy elephant in the room: Is your out-of-distribution detector robust to label noise? [49.88894124047644]
  We take a closer look at 20 state-of-the-art OOD detection methods. We show that poor separation between incorrectly classified ID samples vs. OOD samples is an overlooked yet important limitation of existing methods.
  arXiv  Detail & Related papers  (2024-04-02T09:40:22Z)
• EAT: Towards Long-Tailed Out-of-Distribution Detection [55.380390767978554]
  This paper addresses the challenging task of long-tailed OOD detection. The main difficulty lies in distinguishing OOD data from samples belonging to the tail classes. We propose two simple ideas: (1) Expanding the in-distribution class space by introducing multiple abstention classes, and (2) Augmenting the context-limited tail classes by overlaying images onto the context-rich OOD data.
  arXiv  Detail & Related papers  (2023-12-14T13:47:13Z)
• Large Class Separation is not what you need for Relational Reasoning-based OOD Detection [12.578844450586]
  Out-Of-Distribution (OOD) detection methods provide a solution by identifying semantic novelty. Most of these methods leverage a learning stage on the known data, which means training (or fine-tuning) a model to capture the concept of normality. A viable alternative is that of evaluating similarities in the embedding space produced by large pre-trained models without any further learning effort.
  arXiv  Detail & Related papers  (2023-07-12T14:10:15Z)
• LINe: Out-of-Distribution Detection by Leveraging Important Neurons [15.797257361788812]
  We introduce a new aspect for analyzing the difference in model outputs between in-distribution data and OOD data. We propose a novel method, Leveraging Important Neurons (LINe), for post-hoc Out of distribution detection.
  arXiv  Detail & Related papers  (2023-03-24T13:49:05Z)
• Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning [43.51182049644767]
  Semi-supervised learning (SSL) has long been proved to be an effective technique to construct powerful models with limited labels. Regularization-based methods which force the perturbed samples to have similar predictions with the original ones have attracted much attention. We propose a novel contrastive loss to guide the embedding of the learned network to change linearly between samples.
  arXiv  Detail & Related papers  (2022-02-24T06:00:05Z)
• Active Learning for Open-set Annotation [38.739845944840454]
  We propose a new active learning framework called LfOSA, which boosts the classification performance with an effective sampling strategy to precisely detect examples from known classes for annotation. The experimental results show that the proposed method can significantly improve the selection quality of known classes, and achieve higher classification accuracy with lower annotation cost than state-of-the-art active learning methods.
  arXiv  Detail & Related papers  (2022-01-18T06:11:51Z)
• Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning [101.28281124670647]
  Open-set semi-supervised learning (open-set SSL) investigates a challenging but practical scenario where out-of-distribution (OOD) samples are contained in the unlabeled data. We propose a novel training mechanism that could effectively exploit the presence of OOD data for enhanced feature learning. Our approach substantially lifts the performance on open-set SSL and outperforms the state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2021-08-12T09:14:44Z)
• Towards Reducing Labeling Cost in Deep Object Detection [61.010693873330446]
  We propose a unified framework for active learning, that considers both the uncertainty and the robustness of the detector. Our method is able to pseudo-label the very confident predictions, suppressing a potential distribution drift.
  arXiv  Detail & Related papers  (2021-06-22T16:53:09Z)
• Learning with Out-of-Distribution Data for Audio Classification [60.48251022280506]
  We show that detecting and relabelling certain OOD instances, rather than discarding them, can have a positive effect on learning. The proposed method is shown to improve the performance of convolutional neural networks by a significant margin.
  arXiv  Detail & Related papers  (2020-02-11T21:08:06Z)

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.