Towards Accurate Open-Set Recognition via Background-Class Regularization

• URL: http://arxiv.org/abs/2207.10287v1
• Date: Thu, 21 Jul 2022 03:55:36 GMT
• Title: Towards Accurate Open-Set Recognition via Background-Class Regularization
• Authors: Wonwoo Cho and Jaegul Choo
• Abstract summary: In open-set recognition (OSR), classifiers should be able to reject unknown-class samples while maintaining high closed-set classification accuracy. Previous studies attempted to limit latent feature space and reject data located outside the limited space via offline analyses. We propose a simple inference process (without offline analyses) to conduct OSR in standard classifier architectures. We show that the proposed method provides robust OSR results, while maintaining high closed-set classification accuracy.
• Score: 36.96359929574601
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In open-set recognition (OSR), classifiers should be able to reject unknown-class samples while maintaining high closed-set classification accuracy. To effectively solve the OSR problem, previous studies attempted to limit latent feature space and reject data located outside the limited space via offline analyses, e.g., distance-based feature analyses, or complicated network architectures. To conduct OSR via a simple inference process (without offline analyses) in standard classifier architectures, we use distance-based classifiers instead of conventional Softmax classifiers. Afterwards, we design a background-class regularization strategy, which uses background-class data as surrogates of unknown-class ones during training phase. Specifically, we formulate a novel regularization loss suitable for distance-based classifiers, which reserves sufficiently large class-wise latent feature spaces for known classes and forces background-class samples to be located far away from the limited spaces. Through our extensive experiments, we show that the proposed method provides robust OSR results, while maintaining high closed-set classification accuracy.

Related papers

• Reciprocal Point Learning Network with Large Electromagnetic Kernel for SAR Open-Set Recognition [6.226365654670747]
  Open Set Recognition (OSR) aims to categorize known classes while denoting unknown ones as "unknown" To enhance open-set SAR classification, a method called scattering kernel with reciprocal learning network is proposed. Proposal is made to design convolutional kernels based on large-sized attribute scattering center models.
  arXiv  Detail & Related papers  (2024-11-07T13:26:20Z)
• LORD: Leveraging Open-Set Recognition with Unknown Data [10.200937444995944]
  LORD is a framework to Leverage Open-set Recognition by exploiting unknown data. We identify three model-agnostic training strategies that exploit background data and applied them to well-established classifiers.
  arXiv  Detail & Related papers  (2023-08-24T06:12:41Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Self-Certifying Classification by Linearized Deep Assignment [65.0100925582087]
  We propose a novel class of deep predictors for classifying metric data on graphs within PAC-Bayes risk certification paradigm. Building on the recent PAC-Bayes literature and data-dependent priors, this approach enables learning posterior distributions on the hypothesis space.
  arXiv  Detail & Related papers  (2022-01-26T19:59:14Z)
• Open-Set Recognition: A Good Closed-Set Classifier is All You Need [146.6814176602689]
  We show that the ability of a classifier to make the 'none-of-above' decision is highly correlated with its accuracy on the closed-set classes. We use this correlation to boost the performance of the cross-entropy OSR 'baseline' by improving its closed-set accuracy. We also construct new benchmarks which better respect the task of detecting semantic novelty.
  arXiv  Detail & Related papers  (2021-10-12T17:58:59Z)
• Reconstruction guided Meta-learning for Few Shot Open Set Recognition [31.49168444631114]
  We propose Reconstructing Exemplar-based Few-shot Open-set ClaSsifier (ReFOCS) By using a novel exemplar reconstruction-based meta-learning strategy ReFOCS streamlines FSOSR. We show ReFOCS to outperform multiple state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-07-31T23:23:35Z)
• Hyperspherical embedding for novel class classification [1.5952956981784217]
  We present a constraint-based approach applied to representations in the latent space under the normalized softmax loss. We experimentally validate the proposed approach for the classification of unseen classes on different datasets using both metric learning and the normalized softmax loss. Our results show that not only our proposed strategy can be efficiently trained on larger set of classes, as it does not require pairwise learning, but also present better classification results than the metric learning strategies.
  arXiv  Detail & Related papers  (2021-02-05T15:42:13Z)
• Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier [68.38233199030908]
  Long-tail recognition tackles the natural non-uniformly distributed data in realworld scenarios. While moderns perform well on populated classes, its performance degrades significantly on tail classes. Deep-RTC is proposed as a new solution to the long-tail problem, combining realism with hierarchical predictions.
  arXiv  Detail & Related papers  (2020-07-20T05:57:42Z)
• Capturing scattered discriminative information using a deep architecture in acoustic scene classification [49.86640645460706]
  In this study, we investigate various methods to capture discriminative information and simultaneously mitigate the overfitting problem. We adopt a max feature map method to replace conventional non-linear activations in a deep neural network. Two data augment methods and two deep architecture modules are further explored to reduce overfitting and sustain the system's discriminative power.
  arXiv  Detail & Related papers  (2020-07-09T08:32:06Z)

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.