Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery

• URL: http://arxiv.org/abs/2310.19776v3
• Date: Thu, 18 Jan 2024 17:53:45 GMT
• Title: Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery
• Authors: Sarah Rastegar, Hazel Doughty, Cees G. M. Snoek
• Abstract summary: We propose a novel, efficient and self-supervised method capable of discovering previously unknown categories at test time. A salient feature of our approach is the assignment of minimum length category codes to individual data instances. Experimental evaluations, bolstered by state-of-the-art benchmark comparisons, testify to the efficacy of our solution.
• Score: 49.1865089933055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the quest for unveiling novel categories at test time, we confront the inherent limitations of traditional supervised recognition models that are restricted by a predefined category set. While strides have been made in the realms of self-supervised and open-world learning towards test-time category discovery, a crucial yet often overlooked question persists: what exactly delineates a category? In this paper, we conceptualize a category through the lens of optimization, viewing it as an optimal solution to a well-defined problem. Harnessing this unique conceptualization, we propose a novel, efficient and self-supervised method capable of discovering previously unknown categories at test time. A salient feature of our approach is the assignment of minimum length category codes to individual data instances, which encapsulates the implicit category hierarchy prevalent in real-world datasets. This mechanism affords us enhanced control over category granularity, thereby equipping our model to handle fine-grained categories adeptly. Experimental evaluations, bolstered by state-of-the-art benchmark comparisons, testify to the efficacy of our solution in managing unknown categories at test time. Furthermore, we fortify our proposition with a theoretical foundation, providing proof of its optimality. Our code is available at https://github.com/SarahRastegar/InfoSieve.

Related papers

• SelEx: Self-Expertise in Fine-Grained Generalized Category Discovery [55.72840638180451]
  Generalized Category Discovery aims to simultaneously uncover novel categories and accurately classify known ones. Traditional methods, which lean heavily on self-supervision and contrastive learning, often fall short when distinguishing between fine-grained categories. We introduce a novel concept called self-expertise', which enhances the model's ability to recognize subtle differences and uncover unknown categories.
  arXiv  Detail & Related papers  (2024-08-26T15:53:50Z)
• Blueprinting the Future: Automatic Item Categorization using Hierarchical Zero-Shot and Few-Shot Classifiers [6.907552533477328]
  This study unveils a novel approach employing the zero-shot and few-shot Generative Pretrained Transformer (GPT) for hierarchical item categorization. The hierarchical nature of examination blueprints is navigated seamlessly, allowing for a tiered classification of items across multiple levels. An initial simulation with artificial data demonstrates the efficacy of this method, achieving an average accuracy of 92.91% measured by the F1 score.
  arXiv  Detail & Related papers  (2023-12-06T15:51:49Z)
• Dynamic Conceptional Contrastive Learning for Generalized Category Discovery [76.82327473338734]
  Generalized category discovery (GCD) aims to automatically cluster partially labeled data. Unlabeled data contain instances that are not only from known categories of the labeled data but also from novel categories. One effective way for GCD is applying self-supervised learning to learn discriminate representation for unlabeled data. We propose a Dynamic Conceptional Contrastive Learning framework, which can effectively improve clustering accuracy.
  arXiv  Detail & Related papers  (2023-03-30T14:04:39Z)
• Parametric Classification for Generalized Category Discovery: A Baseline Study [70.73212959385387]
  Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples. We investigate the failure of parametric classifiers, verify the effectiveness of previous design choices when high-quality supervision is available, and identify unreliable pseudo-labels as a key problem. We propose a simple yet effective parametric classification method that benefits from entropy regularisation, achieves state-of-the-art performance on multiple GCD benchmarks and shows strong robustness to unknown class numbers.
  arXiv  Detail & Related papers  (2022-11-21T18:47:11Z)
• Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning [37.6512548064269]
  We investigate a new practical scenario called Fine-grained Category Discovery under Coarse-grained supervision (FCDC) FCDC aims at discovering fine-grained categories with only coarse-grained labeled data, which can adapt models to categories of different granularity from known ones and reduce significant labeling cost. We propose a hierarchical weighted self-contrastive network by building a novel weighted self-contrastive module and combining it with supervised learning in a hierarchical manner.
  arXiv  Detail & Related papers  (2022-10-14T12:06:23Z)
• XCon: Learning with Experts for Fine-grained Category Discovery [4.787507865427207]
  We present a novel method called Expert-Contrastive Learning (XCon) to help the model to mine useful information from the images. Experiments on fine-grained datasets show a clear improved performance over the previous best methods, indicating the effectiveness of our method.
  arXiv  Detail & Related papers  (2022-08-03T08:03:12Z)
• Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning [68.63910949916209]
  This paper tackles the problem of novel category discovery (NCD), which aims to discriminate unknown categories in large-scale image collections. We propose a novel adaptive prototype learning method consisting of two main stages: prototypical representation learning and prototypical self-training. We conduct extensive experiments on four benchmark datasets and demonstrate the effectiveness and robustness of the proposed method with state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-08-01T16:34:33Z)
• 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)

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.