给不同的class 产生单独对应的cluster就更完美了,后者相对于前者的简单的分类任务更困难,但最终得到的模型也更加鲁棒,因为当模型不仅能正确区分不同类别,并且可以给每个class产生独立的cluster(即不仅增大了类间距离,同时也减少了类内距离,从而使得整个representation spaces非常的紧凑而分明),那么这样的决策平面会更加rob...
This work focuses on the problem of Generalized Category Discovery (GCD), a more realistic and challenging semi-supervised learning setting where unlabeled data may belong to either previously known or unseen categories. Recent advancements have demonstrated the efficacy of both pseudo-label-based ...
代码链接:DTennant/Incremental-Generalized-Category-Discovery (github.com) 摘要 我们探讨了增量通用类别发现(IGCD)问题。这是一个极具挑战性的增量分类学习问题,其目标是开发出能够正确分类以前所见类别图像的模型,同时发现新的类别。学习是在一系列时间步骤中进行的,模型在每次迭代时都会获得新的标记和未标记数据,并...
Generalized Category Discovery (GCD) is a crucial task that aims to recognize both known and novel categories from a set of unlabeled data by utilizing a few labeled data with only known categories. Due to the lack of supervision and category information, current methods usually perform poorly on...
Generalized Category Discovery (GCD) aims at discovering both known and unknown classes in unlabeled data, using the knowledge learned from a limited set of labeled data. Despite today’s foundation models being trained with Internet-scale multi-modal corpus, we find that they still struggle in GC...
GCDSS - Generalized Category Discovery in Semantic Segmentation Welcome to the official repository for the Generalized Category Discovery in Semantic Segmentation (GCDSS) project! Overview This repository contains the code implementation for GCDSS. We are currently in the process of organizing and refinin...
To address these challenges, we propose a novel computational framework, named AnnoGCD, building on Generalized Category Discovery (GCD) and Anomaly Detection (AD) for automatic cell type annotation. Our semi-supervised method combines labeled and unlabeled data to accurately classify known cell types...
Generalized Category Discovery (GCD) utilizes labelled data from seen categories to cluster unlabelled samples from both seen and unseen categories. Previous methods have demonstrated that assigning pseudo-labels for representation learning is effective. However, these methods commonly predict pseudo-labels ...
论文链接:[2305.06144] Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery (arxiv.org) 代码链接:github.com/DTennant/GPC 这篇文章中忽略了许多细节推导问题,事实上是引用了聚类中使用GMM的工作,需要理解细节的可以参考DeepDPM arxiv.org/abs/2203.1430。其实笔者也关注过DeepDPM,但...
论文链接:[2305.10420] CLIP-GCD: Simple Language Guided Generalized Category Discovery (arxiv.org) 目前未公布代码,idea有点意思,就是文章感觉还不太完整,可以蹲个完整版。 摘要 广义类别发现(GCD)需要一个模型来对未标记数据中的已知类别进行分类并对未知类别进行聚类。先前的方法利用自监督预训练结合对标记数据...