Grow and Merge: A Unified Framework for Continuous Categories Discovery
- URL: http://arxiv.org/abs/2210.04174v1
- Date: Sun, 9 Oct 2022 05:49:03 GMT
- Title: Grow and Merge: A Unified Framework for Continuous Categories Discovery
- Authors: Xinwei Zhang, Jianwen Jiang, Yutong Feng, Zhi-Fan Wu, Xibin Zhao, Hai
Wan, Mingqian Tang, Rong Jin, Yue Gao
- Abstract summary: We focus on the application scenarios where unlabeled data are continuously fed into the category discovery system.
We develop a framework of bf Grow and Merge (bf GM) that works by alternating between a growing phase and a merging phase.
In the growing phase, it increases the diversity of features through a continuous self-supervised learning for effective category mining.
In the merging phase, it merges the grown model with a static one to ensure satisfying performance for known classes.
- Score: 44.28297337872006
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Although a number of studies are devoted to novel category discovery, most of
them assume a static setting where both labeled and unlabeled data are given at
once for finding new categories. In this work, we focus on the application
scenarios where unlabeled data are continuously fed into the category discovery
system. We refer to it as the {\bf Continuous Category Discovery} ({\bf CCD})
problem, which is significantly more challenging than the static setting. A
common challenge faced by novel category discovery is that different sets of
features are needed for classification and category discovery: class
discriminative features are preferred for classification, while rich and
diverse features are more suitable for new category mining. This challenge
becomes more severe for dynamic setting as the system is asked to deliver good
performance for known classes over time, and at the same time continuously
discover new classes from unlabeled data. To address this challenge, we develop
a framework of {\bf Grow and Merge} ({\bf GM}) that works by alternating
between a growing phase and a merging phase: in the growing phase, it increases
the diversity of features through a continuous self-supervised learning for
effective category mining, and in the merging phase, it merges the grown model
with a static one to ensure satisfying performance for known classes. Our
extensive studies verify that the proposed GM framework is significantly more
effective than the state-of-the-art approaches for continuous category
discovery.
Related papers
- Generative Multi-modal Models are Good Class-Incremental Learners [51.5648732517187]
We propose a novel generative multi-modal model (GMM) framework for class-incremental learning.
Our approach directly generates labels for images using an adapted generative model.
Under the Few-shot CIL setting, we have improved by at least 14% accuracy over all the current state-of-the-art methods with significantly less forgetting.
arXiv Detail & Related papers (2024-03-27T09:21:07Z) - Learn to Categorize or Categorize to Learn? Self-Coding for Generalized
Category Discovery [49.1865089933055]
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.
arXiv Detail & Related papers (2023-10-30T17:45:32Z) - Towards Novel Class Discovery: A Study in Novel Skin Lesions Clustering [22.24175320515204]
We propose a new novel class discovery framework for automatically discovering new semantic classes from dermoscopy image datasets.
Specifically, we first use contrastive learning to learn a robust and unbiased feature representation based on all data from known and unknown categories.
We conducted extensive experiments on the dermatology dataset ISIC 2019, and the experimental results show that our approach can effectively leverage knowledge from known categories to discover new semantic categories.
arXiv Detail & Related papers (2023-09-28T13:59:29Z) - 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) - PatchMix Augmentation to Identify Causal Features in Few-shot Learning [55.64873998196191]
Few-shot learning aims to transfer knowledge learned from base with sufficient categories labelled data to novel categories with scarce known information.
We propose a novel data augmentation strategy dubbed as PatchMix that can break this spurious dependency.
We show that such an augmentation mechanism, different from existing ones, is able to identify the causal features.
arXiv Detail & Related papers (2022-11-29T08:41:29Z) - 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) - 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)
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.