Novel Class Discovery for Long-tailed Recognition

• URL: http://arxiv.org/abs/2308.02989v3
• Date: Fri, 25 Aug 2023 13:57:03 GMT
• Title: Novel Class Discovery for Long-tailed Recognition
• Authors: Chuyu Zhang, Ruijie Xu, Xuming He
• Abstract summary: We consider a more realistic setting for novel class discovery where the distributions of novel and known classes are long-tailed. To tackle this problem, we propose an adaptive self-labeling strategy based on an equiangular prototype representation of classes. Our method infers high-quality pseudo-labels for the novel classes by solving a relaxed optimal transport problem.
• Score: 17.464885951359047
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: While the novel class discovery has recently made great progress, existing methods typically focus on improving algorithms on class-balanced benchmarks. However, in real-world recognition tasks, the class distributions of their corresponding datasets are often imbalanced, which leads to serious performance degeneration of those methods. In this paper, we consider a more realistic setting for novel class discovery where the distributions of novel and known classes are long-tailed. One main challenge of this new problem is to discover imbalanced novel classes with the help of long-tailed known classes. To tackle this problem, we propose an adaptive self-labeling strategy based on an equiangular prototype representation of classes. Our method infers high-quality pseudo-labels for the novel classes by solving a relaxed optimal transport problem and effectively mitigates the class biases in learning the known and novel classes. We perform extensive experiments on CIFAR100, ImageNet100, Herbarium19 and large-scale iNaturalist18 datasets, and the results demonstrate the superiority of our method. Our code is available at https://github.com/kleinzcy/NCDLR.

Related papers

• Happy: A Debiased Learning Framework for Continual Generalized Category Discovery [54.54153155039062]
  This paper explores the underexplored task of Continual Generalized Category Discovery (C-GCD) C-GCD aims to incrementally discover new classes from unlabeled data while maintaining the ability to recognize previously learned classes. We introduce a debiased learning framework, namely Happy, characterized by Hardness-aware prototype sampling and soft entropy regularization.
  arXiv  Detail & Related papers  (2024-10-09T04:18:51Z)
• Active Generalized Category Discovery [60.69060965936214]
  Generalized Category Discovery (GCD) endeavors to cluster unlabeled samples from both novel and old classes. We take the spirit of active learning and propose a new setting called Active Generalized Category Discovery (AGCD) Our method achieves state-of-the-art performance on both generic and fine-grained datasets.
  arXiv  Detail & Related papers  (2024-03-07T07:12:24Z)
• Class-relation Knowledge Distillation for Novel Class Discovery [16.461242381109276]
  Key challenge lies in transferring the knowledge in the known-class data to the learning of novel classes. We introduce a class relation representation for the novel classes based on the predicted class distribution of a model trained on known classes. We propose a novel knowledge distillation framework, which utilizes our class-relation representation to regularize the learning of novel classes.
  arXiv  Detail & Related papers  (2023-07-18T11:35:57Z)
• Class-Incremental Learning: A Survey [84.30083092434938]
  Class-Incremental Learning (CIL) enables the learner to incorporate the knowledge of new classes incrementally. CIL tends to catastrophically forget the characteristics of former ones, and its performance drastically degrades. We provide a rigorous and unified evaluation of 17 methods in benchmark image classification tasks to find out the characteristics of different algorithms.
  arXiv  Detail & Related papers  (2023-02-07T17:59:05Z)
• Class-incremental Novel Class Discovery [76.35226130521758]
  We study the new task of class-incremental Novel Class Discovery (class-iNCD) We propose a novel approach for class-iNCD which prevents forgetting of past information about the base classes. Our experiments, conducted on three common benchmarks, demonstrate that our method significantly outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-07-18T13:49:27Z)
• Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection [56.22467011292147]
  Several incremental learning methods are proposed to mitigate catastrophic forgetting for object detection. Despite the effectiveness, these methods require co-occurrence of the unlabeled base classes in the training data of the novel classes. We propose the use of unlabeled in-the-wild data to bridge the non-occurrence caused by the missing base classes during the training of additional novel classes.
  arXiv  Detail & Related papers  (2021-10-28T10:57:25Z)
• ClaRe: Practical Class Incremental Learning By Remembering Previous Class Representations [9.530976792843495]
  Class Incremental Learning (CIL) tends to learn new concepts perfectly, but not at the expense of performance and accuracy for old data. ClaRe is an efficient solution for CIL by remembering the representations of learned classes in each increment. ClaRe has a better generalization than prior methods thanks to producing diverse instances from the distribution of previously learned classes.
  arXiv  Detail & Related papers  (2021-03-29T10:39:42Z)
• Open-World Semi-Supervised Learning [66.90703597468377]
  We introduce a new open-world semi-supervised learning setting in which the model is required to recognize previously seen classes. We propose ORCA, an approach that learns to simultaneously classify and cluster the data. We demonstrate that ORCA accurately discovers novel classes and assigns samples to previously seen classes on benchmark image classification datasets.
  arXiv  Detail & Related papers  (2021-02-06T07:11:07Z)
• Class-incremental Learning with Rectified Feature-Graph Preservation [24.098892115785066]
  A central theme of this paper is to learn new classes that arrive in sequential phases over time. We propose a weighted-Euclidean regularization for old knowledge preservation. We show how it can work with binary cross-entropy to increase class separation for effective learning of new classes.
  arXiv  Detail & Related papers  (2020-12-15T07:26:04Z)

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.