Open-world semi-supervised learning (OWSSL) extends conventional
semi-supervised learning to open-world scenarios by taking account of novel
categories in unlabeled datasets. Despite the recent advancements in OWSSL, the
success often relies on the assumptions that 1) labeled and unlabeled datasets
share the same balanced class prior distribution, which does not generally hold
in real-world applications, and 2) unlabeled training datasets are utilized for
evaluation, where such transductive inference might not adequately address
challenges in the wild. In this paper, we aim to generalize OWSSL by addressing
them. Our work suggests that practical OWSSL may require different training
settings, evaluation methods, and learning strategies compared to those
prevalent in the existing literature.

该研究通过解决开放世界半监督学习中的假设假设和评估方法的问题，以扩展实用的开放世界半监督学习。

重新思考开放世界半监督学习：分布不匹配和归纳推理

Rethinking Open-World Semi-Supervised Learning: Distribution Mismatch  and Inductive Inference

Open-world semi-supervised learning (Open-world SSL) for node classification,
that classifies unlabeled nodes into seen classes or multiple novel classes, is
a practical but under-explored problem in the graph community. As only seen
classes have human labels, they are usually better learned than novel classes,
and thus exhibit smaller intra-class variances within the embedding space
(named as imbalance of intra-class variances between seen and novel classes).
Based on empirical and theoretical analysis, we find the variance imbalance can
negatively impact the model performance. Pre-trained feature encoders can
alleviate this issue via producing compact representations for novel classes.
However, creating general pre-trained encoders for various types of graph data
has been proven to be challenging. As such, there is a demand for an effective
method that does not rely on pre-trained graph encoders. In this paper, we
propose an IMbalance-Aware method named OpenIMA for Open-world semi-supervised
node classification, which trains the node classification model from scratch
via contrastive learning with bias-reduced pseudo labels. Extensive experiments
on seven popular graph benchmarks demonstrate the effectiveness of OpenIMA, and
the source code has been available on GitHub.

OpenIMA 是一个不依赖预训练图编码器的鲁棒性、基于对比学习的方法，用于从头开始训练节点分类模型并通过减小偏差的伪标签来解决开放世界半监督节点分类问题。

开放式半监督学习用于节点分类

Open-World Semi-Supervised Learning for Node Classification

Open-world semi-supervised learning aims at inferring both known and novel
classes in unlabeled data, by harnessing prior knowledge from a labeled set
with known classes. Despite its importance, there is a lack of theoretical
foundations for this problem. This paper bridges the gap by formalizing a
graph-theoretic framework tailored for the open-world setting, where the
clustering can be theoretically characterized by graph factorization. Our
graph-theoretic framework illuminates practical algorithms and provides
guarantees. In particular, based on our graph formulation, we apply the
algorithm called Spectral Open-world Representation Learning (SORL), and show
that minimizing our loss is equivalent to performing spectral decomposition on
the graph. Such equivalence allows us to derive a provable error bound on the
clustering performance for both known and novel classes, and analyze rigorously
when labeled data helps. Empirically, SORL can match or outperform several
strong baselines on common benchmark datasets, which is appealing for practical
usage while enjoying theoretical guarantees.

这篇论文以图论为基础，提出了开放世界半监督学习的理论框架，并在此基础上设计了一种叫做 “光谱开放世界表示学习” 的算法，用于从已标记数据中推断出未知类别的未标记数据，并且能在已知和未知类别的聚类任务中提供可靠的理论保证。

一个用于理解开放世界半监督学习的图论框架

A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised  Learning

In open-world semi-supervised learning, a machine learning model is tasked
with uncovering novel categories from unlabeled data while maintaining
performance on seen categories from labeled data. The central challenge is the
substantial learning gap between seen and novel categories, as the model learns
the former faster due to accurate supervisory information. To address this, we
introduce 1) an adaptive margin loss based on estimated class distribution,
which encourages a large negative margin for samples in seen classes, to
synchronize learning paces, and 2) pseudo-label contrastive clustering, which
pulls together samples which are likely from the same class in the output
space, to enhance novel class discovery. Our extensive evaluations on multiple
datasets demonstrate that existing models still hinder novel class learning,
whereas our approach strikingly balances both seen and novel classes, achieving
a remarkable 3% average accuracy increase on the ImageNet dataset compared to
the prior state-of-the-art. Additionally, we find that fine-tuning the
self-supervised pre-trained backbone significantly boosts performance over the
default in prior literature. After our paper is accepted, we will release the
code.

开放世界半监督学习中，通过估计类别分布的自适应边界损失和伪标签对比聚类的引入，解决了已有模型在学习新类别方面的困难，平衡了已见类别与新类别，并在 ImageNet 数据集上相较之前的最新技术提高了 3% 的平均准确率。