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。