Generalized Category Discovery (GCD) aims to recognize both known and novel
categories from a set of unlabeled data, based on another dataset labeled with
only known categories. Without considering differences between known and novel
categories, current methods learn about them in a coupled manner, which can
hurt model's generalization and discriminative ability. Furthermore, the
coupled training approach prevents these models transferring category-specific
knowledge explicitly from labeled data to unlabeled data, which can lose
high-level semantic information and impair model performance. To mitigate above
limitations, we present a novel model called Decoupled Prototypical Network
(DPN). By formulating a bipartite matching problem for category prototypes, DPN
can not only decouple known and novel categories to achieve different training
targets effectively, but also align known categories in labeled and unlabeled
data to transfer category-specific knowledge explicitly and capture high-level
semantics. Furthermore, DPN can learn more discriminative features for both
known and novel categories through our proposed Semantic-aware Prototypical
Learning (SPL). Besides capturing meaningful semantic information, SPL can also
alleviate the noise of hard pseudo labels through semantic-weighted soft
assignment. Extensive experiments show that DPN outperforms state-of-the-art
models by a large margin on all evaluation metrics across multiple benchmark
datasets. Code and data are available at this https URL.

提出了一种名为 Decoupled Prototypical Network 的新模型，能够解耦已知和新类别，显式地从标记数据到未标记数据转移类别特定知识，捕捉高级语义信息，并通过语义加权软赋值减轻硬伪标签噪声，该模型在多个基准数据集上表现优于现有技术。

基于分离原型网络的泛化类别发现

Generalized Category Discovery with Decoupled Prototypical Network

In this paper, we consider a highly general image recognition setting
wherein, given a labelled and unlabelled set of images, the task is to
categorize all images in the unlabelled set. Here, the unlabelled images may
come from labelled classes or from novel ones. Existing recognition methods are
not able to deal with this setting, because they make several restrictive
assumptions, such as the unlabelled instances only coming from known - or
unknown - classes, and the number of unknown classes being known a-priori. 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
transformers with contrastive representation learning for this open-world
setting. We then introduce a simple yet effective semi-supervised $k$-means
method to cluster the unlabelled data into seen and unseen classes
automatically, substantially outperforming the baselines. Finally, we also
propose a new approach to estimate the number of classes in the unlabelled
data. We thoroughly evaluate our approach on public datasets for generic object
classification and on fine-grained datasets, leveraging the recent Semantic
Shift Benchmark suite. Project page at
this https URL

本文研究了一种高度通用的图像识别场景，提出了一种名为 'Generalized Category Discovery' 的方法，使用视觉变换器、对比表示学习和半监督 k 均值方法对未标记的数据进行分类。