如上图,在深度学习中,分类任务对于representation space产生的效果,主要是类间距离的增大,本身并没有对类内距离进行约束,因此,如果一个多分类的任务的acc非常完美,训练产生的representation space并不一定适合聚类,例如上图中的左图,这是因为在representation space中,如果不同class之间已经有较为明显的分界面之后,model...
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...
We address the more unconstrained setting, naming it 'Generalized Category Discovery', and challenge all these assumptions. We first establish strong baselines by taking state-of-the-art algorithms from novel category discovery and adapting them for this task. Next, we propose the use of vision ...
Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery Abstract 在本文中,我们解决了广义类别发现(generalized category discovery, GCD)的问题,即给定一组图像,其中一部分被标记,其余部分未标记,任务是利用来自有标签数据的信息,在无标签数据中自动聚类图像,而无标签数据包含来自标记类的...
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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 to identify a mix of known and novel categories within unlabeled data sets, providing a more realistic setting for image recognition. Essentially, GCD needs to remember existing patterns thoroughly to recognize novel categories. Recent state-of-the-art method...
PyTorch implementation of our paper: Active Generalized Category Discovery (CVPR 2024)New Setting 🌟To address the inherent issues of Generalized Category Discovery (GCD), including imbalanced classification performance and inconsistent confidence between old and new classes, we take the spirit of Active...
One major conundrum with both Generalized Category Discovery and Novel Category Discovery is that the definition of the category has remained undetermined. This complication can be overlooked when the granularity of categories at test time is similar to training time. However, for more realistic applica...
论文地址:[2211.11727] Parametric Classification for Generalized Category Discovery: A Baseline Study (arxiv.org) 代码地址:CVMI-Lab/SimGCD: (ICCV 2023) Parametric Classification for Generalized Cat…