NC-NCD: Novel Class Discovery for Node Classification

• URL: http://arxiv.org/abs/2407.17816v1
• Date: Thu, 25 Jul 2024 07:10:08 GMT
• Title: NC-NCD: Novel Class Discovery for Node Classification
• Authors: Yue Hou, Xueyuan Chen, He Zhu, Romei Liu, Bowen Shi, Jiaheng Liu, Junran Wu, Ke Xu,
• Abstract summary: Class Discovery (NCD) involves identifying new categories within unlabeled data by utilizing knowledge acquired from previously established categories. Existing NCD methods often struggle to maintain a balance between the performance of old and new categories. We introduce for the first time a more practical NCD scenario for node classification (i.e., NC-NCD) We propose a novel self-training framework with prototype replay and distillation called SWORD, adopted to our NC-NCD setting.
• Score: 28.308556235456766
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Novel Class Discovery (NCD) involves identifying new categories within unlabeled data by utilizing knowledge acquired from previously established categories. However, existing NCD methods often struggle to maintain a balance between the performance of old and new categories. Discovering unlabeled new categories in a class-incremental way is more practical but also more challenging, as it is frequently hindered by either catastrophic forgetting of old categories or an inability to learn new ones. Furthermore, the implementation of NCD on continuously scalable graph-structured data remains an under-explored area. In response to these challenges, we introduce for the first time a more practical NCD scenario for node classification (i.e., NC-NCD), and propose a novel self-training framework with prototype replay and distillation called SWORD, adopted to our NC-NCD setting. Our approach enables the model to cluster unlabeled new category nodes after learning labeled nodes while preserving performance on old categories without reliance on old category nodes. SWORD achieves this by employing a self-training strategy to learn new categories and preventing the forgetting of old categories through the joint use of feature prototypes and knowledge distillation. Extensive experiments on four common benchmarks demonstrate the superiority of SWORD over other state-of-the-art methods.

Related papers

• 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)
• 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)
• RECALL: Rehearsal-free Continual Learning for Object Classification [24.260824374268314]
  Convolutional neural networks show remarkable results in classification but struggle with learning new things on the fly. We present a novel rehearsal-free approach, where a deep neural network is continually learning new unseen object categories. Our approach is called RECALL, as the network recalls categories by calculating logits for old categories before training new ones.
  arXiv  Detail & Related papers  (2022-09-29T13:36:28Z)
• 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)
• Novel Class Discovery without Forgetting [72.52222295216062]
  We identify and formulate a new, pragmatic problem setting of NCDwF: Novel Class Discovery without Forgetting. We propose a machine learning model to incrementally discover novel categories of instances from unlabeled data. We introduce experimental protocols based on CIFAR-10, CIFAR-100 and ImageNet-1000 to measure the trade-off between knowledge retention and novel class discovery.
  arXiv  Detail & Related papers  (2022-07-21T17:54:36Z)
• 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)
• Novel Class Discovery in Semantic Segmentation [104.30729847367104]
  We introduce a new setting of Novel Class Discovery in Semantic (NCDSS) It aims at segmenting unlabeled images containing new classes given prior knowledge from a labeled set of disjoint classes. In NCDSS, we need to distinguish the objects and background, and to handle the existence of multiple classes within an image. We propose the Entropy-based Uncertainty Modeling and Self-training (EUMS) framework to overcome noisy pseudo-labels.
  arXiv  Detail & Related papers  (2021-12-03T13:31:59Z)
• Hierarchical Prototype Networks for Continual Graph Representation Learning [90.78466005753505]
  We present Hierarchical Prototype Networks (HPNs) which extract different levels of abstract knowledge in the form of prototypes to represent the continuously expanded graphs. We show that HPNs not only outperform state-of-the-art baseline techniques but also consume relatively less memory.
  arXiv  Detail & Related papers  (2021-11-30T14:15:14Z)

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.