Traditional semi-supervised learning (SSL) assumes that the feature
distributions of labeled and unlabeled data are consistent which rarely holds
in realistic scenarios. In this paper, we propose a novel SSL setting, where
unlabeled samples are drawn from a mixed distribution that deviates from the
feature distribution of labeled samples. Under this setting, previous SSL
methods tend to predict wrong pseudo-labels with the model fitted on labeled
data, resulting in noise accumulation. To tackle this issue, we propose
Self-Supervised Feature Adaptation (SSFA), a generic framework for improving
SSL performance when labeled and unlabeled data come from different
distributions. SSFA decouples the prediction of pseudo-labels from the current
model to improve the quality of pseudo-labels. Particularly, SSFA incorporates
a self-supervised task into the SSL framework and uses it to adapt the feature
extractor of the model to the unlabeled data. In this way, the extracted
features better fit the distribution of unlabeled data, thereby generating
high-quality pseudo-labels. Extensive experiments show that our proposed SSFA
is applicable to various pseudo-label-based SSL learners and significantly
improves performance in labeled, unlabeled, and even unseen distributions.

通过提出自监督特征适应（SSFA）方法，解决了传统半监督学习在标记和未标记数据分布不一致时预测错误伪标签的问题，从而提高了半监督学习性能。

自监督特征适应的广义半监督学习

Generalized Semi-Supervised Learning via Self-Supervised Feature  Adaptation

Industrial anomaly detection is generally addressed as an unsupervised task
that aims at locating defects with only normal training samples. Recently,
numerous 2D anomaly detection methods have been proposed and have achieved
promising results, however, using only the 2D RGB data as input is not
sufficient to identify imperceptible geometric surface anomalies. Hence, in
this work, we focus on multi-modal anomaly detection. Specifically, we
investigate early multi-modal approaches that attempted to utilize models
pre-trained on large-scale visual datasets, i.e., ImageNet, to construct
feature databases. And we empirically find that directly using these
pre-trained models is not optimal, it can either fail to detect subtle defects
or mistake abnormal features as normal ones. This may be attributed to the
domain gap between target industrial data and source data.Towards this problem,
we propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method
to finetune the adaptors and learn task-oriented representation toward anomaly
detection.Both intra-modal adaptation and cross-modal alignment are optimized
from a local-to-global perspective in LSFA to ensure the representation quality
and consistency in the inference stage.Extensive experiments demonstrate that
our method not only brings a significant performance boost to feature embedding
based approaches, but also outperforms previous State-of-The-Art (SoTA) methods
prominently on both MVTec-3D AD and Eyecandies datasets, e.g., LSFA achieves
97.1% I-AUROC on MVTec-3D, surpass previous SoTA by +3.4%.

通过多模态数据和自监督特征适应方法，本文在工业异常检测领域提出了一种局部到全局的自适应特征调整方法，显著提高了特征嵌入方法的性能，并取得了超越之前最先进方法的显著成果。