Semi-supervised learning (SSL) is a practical challenge in computer vision.
Pseudo-label (PL) methods, e.g., FixMatch and FreeMatch, obtain the State Of
The Art (SOTA) performances in SSL. These approaches employ a
threshold-to-pseudo-label (T2L) process to generate PLs by truncating the
confidence scores of unlabeled data predicted by the self-training method.
However, self-trained models typically yield biased and high-variance
predictions, especially in the scenarios when a little labeled data are
supplied. To address this issue, we propose a lightweight channel-based
ensemble method to effectively consolidate multiple inferior PLs into the
theoretically guaranteed unbiased and low-variance one. Importantly, our
approach can be readily extended to any SSL framework, such as FixMatch or
FreeMatch. Experimental results demonstrate that our method significantly
outperforms state-of-the-art techniques on CIFAR10/100 in terms of
effectiveness and efficiency.

我们提出了一种轻量级通道级集成方法，用于将多个次优的伪标签有效地合并为理论上保证的无偏差和低方差的伪标签，该方法可轻松扩展到任何半监督学习框架，并在 CIFAR10/100 上显著优于现有技术，无论是在效果上还是效率上。

一种通道集合方法：无偏差和低方差的伪标签对半监督分类至关重要

A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is  Critical for Semi-supervised Classification

Recent advances in whole-slide image (WSI) scanners and computational
capabilities have significantly propelled the application of artificial
intelligence in histopathology slide analysis. While these strides are
promising, current supervised learning approaches for WSI analysis come with
the challenge of exhaustively labeling high-resolution slides - a process that
is both labor-intensive and time-consuming. In contrast, self-supervised
learning (SSL) pretraining strategies are emerging as a viable alternative,
given that they don't rely on explicit data annotations. These SSL strategies
are quickly bridging the performance disparity with their supervised
counterparts. In this context, we introduce an SSL framework. This framework
aims for transferable representation learning and semantically meaningful
clustering by synergizing invariance loss and clustering loss in WSI analysis.
Notably, our approach outperforms common SSL methods in downstream
classification and clustering tasks, as evidenced by tests on the Camelyon16
and a pancreatic cancer dataset. The code and additional details are accessible
at: this https URL

最近整个切片图像（WSI）扫描仪和计算能力的进展显著推动了人工智能在组织病理学切片分析中的应用。

利用聚类约束改善组织病理学图像的表征学习

Improving Representation Learning for Histopathologic Images with  Cluster Constraints

With the progress of sensor technology in wearables, the collection and
analysis of PPG signals are gaining more interest. Using Machine Learning, the
cardiac rhythm corresponding to PPG signals can be used to predict different
tasks such as activity recognition, sleep stage detection, or more general
health status. However, supervised learning is often limited by the amount of
available labeled data, which is typically expensive to obtain. To address this
problem, we propose a Self-Supervised Learning (SSL) method with a pretext task
of signal reconstruction to learn an informative generalized PPG
representation. The performance of the proposed SSL framework is compared with
two fully supervised baselines. The results show that in a very limited label
data setting (10 samples per class or less), using SSL is beneficial, and a
simple classifier trained on SSL-learned representations outperforms fully
supervised deep neural networks. However, the results reveal that the
SSL-learned representations are too focused on encoding the subjects.
Unfortunately, there is high inter-subject variability in the SSL-learned
representations, which makes working with this data more challenging when
labeled data is scarce. The high inter-subject variability suggests that there
is still room for improvements in learning representations. In general, the
results suggest that SSL may pave the way for the broader use of machine
learning models on PPG data in label-scarce regimes.

本文提出了一种自监督学习方法用于学习一个通用的、具有信息的 PPG 表示，并将其用于活动和睡眠阶段的识别以及更普遍的健康状态预测，结果表明在标记数据稀缺时，使用 SSL 对于简单分类器的训练表现更好，同时也发现 SSL 的结果集中于编码受试者，因此对于标记数据不足的情况有一定局限性。