Generalized category discovery (GCD) is a recently proposed open-world task.
Given a set of images consisting of labeled and unlabeled instances, the goal
of GCD is to automatically cluster the unlabeled samples using information
transferred from the labeled dataset. The unlabeled dataset comprises both
known and novel classes. The main challenge is that unlabeled novel class
samples and unlabeled known class samples are mixed together in the unlabeled
dataset. To address the GCD without knowing the class number of unlabeled
dataset, we propose a co-training-based framework that encourages clustering
consistency. Specifically, we first introduce weak and strong augmentation
transformations to generate two sufficiently different views for the same
sample. Then, based on the co-training assumption, we propose a consistency
representation learning strategy, which encourages consistency between
feature-prototype similarity and clustering assignment. Finally, we use the
discriminative embeddings learned from the semi-supervised representation
learning process to construct an original sparse network and use a community
detection method to obtain the clustering results and the number of categories
simultaneously. Extensive experiments show that our method achieves
state-of-the-art performance on three generic benchmarks and three fine-grained
visual recognition datasets. Especially in the ImageNet-100 data set, our
method significantly exceeds the best baseline by 15.5\% and 7.0\% on the
\texttt{Novel} and \texttt{All} classes, respectively.

自动发现广义类别的开放式任务，使用半监督表示学习过程中获得的区分性嵌入，构建原始稀疏网络并使用社区检测方法同时获取聚类结果和类别数。

使用聚类分配一致性进行的广义类别发现

Generalized Category Discovery with Clustering Assignment Consistency

The graph Laplacian regularization term is usually used in semi-supervised
representation learning to provide graph structure information for a model
$f(X)$. However, with the recent popularity of graph neural networks (GNNs),
directly encoding graph structure $A$ into a model, i.e., $f(A, X)$, has become
the more common approach. While we show that graph Laplacian regularization
brings little-to-no benefit to existing GNNs, and propose a simple but
non-trivial variant of graph Laplacian regularization, called
Propagation-regularization (P-reg), to boost the performance of existing GNN
models. We provide formal analyses to show that P-reg not only infuses extra
information (that is not captured by the traditional graph Laplacian
regularization) into GNNs, but also has the capacity equivalent to an
infinite-depth graph convolutional network. We demonstrate that P-reg can
effectively boost the performance of existing GNN models on both node-level and
graph-level tasks across many different datasets.

在图神经网络中，传统的图拉普拉斯正则化对现有 GNN 的性能提升不大，提出了一种名叫 P-reg 的图拉普拉斯正则化变体，可以有效增加信息熵，提升现有 GNN 模型在节点级和图级任务上的性能。

重新思考图神经网络的图正则化

Rethinking Graph Regularization for Graph Neural Networks

Unsupervised text embedding methods, such as Skip-gram and Paragraph Vector,
have been attracting increasing attention due to their simplicity, scalability,
and effectiveness. However, comparing to sophisticated deep learning
architectures such as convolutional neural networks, these methods usually
yield inferior results when applied to particular machine learning tasks. One
possible reason is that these text embedding methods learn the representation
of text in a fully unsupervised way, without leveraging the labeled information
available for the task. Although the low dimensional representations learned
are applicable to many different tasks, they are not particularly tuned for any
task. In this paper, we fill this gap by proposing a semi-supervised
representation learning method for text data, which we call the
\textit{predictive text embedding} (PTE). Predictive text embedding utilizes
both labeled and unlabeled data to learn the embedding of text. The labeled
information and different levels of word co-occurrence information are first
represented as a large-scale heterogeneous text network, which is then embedded
into a low dimensional space through a principled and efficient algorithm. This
low dimensional embedding not only preserves the semantic closeness of words
and documents, but also has a strong predictive power for the particular task.
Compared to recent supervised approaches based on convolutional neural
networks, predictive text embedding is comparable or more effective, much more
efficient, and has fewer parameters to tune.

本文提出了一种名为预测性文本嵌入的半监督表示学习方法，它利用带标签和无标签数据来学习文本的嵌入，并将其表示为一种大规模异构文本网络，有效地降低了参数数量和效率。