Despite the progress of Semi-supervised Learning (SSL), existing methods fail
to utilize unlabeled data effectively and efficiently. Many pseudo-label-based
methods select unlabeled examples based on inaccurate confidence scores from
the classifier. Most prior work also uses all available unlabeled data without
pruning, making it difficult to handle large amounts of unlabeled data. To
address these issues, we propose two methods: Variational Confidence
Calibration (VCC) and Influence-Function-based Unlabeled Sample Elimination
(INFUSE). VCC is an universal plugin for SSL confidence calibration, using a
variational autoencoder to select more accurate pseudo labels based on three
types of consistency scores. INFUSE is a data pruning method that constructs a
core dataset of unlabeled examples under SSL. Our methods are effective in
multiple datasets and settings, reducing classification errors rates and saving
training time. Together, VCC-INFUSE reduces the error rate of FlexMatch on the
CIFAR-100 dataset by 1.08% while saving nearly half of the training time.

我们提出了两种方法：Variational Confidence Calibration (VCC) 和 Influence-Function-based Unlabeled Sample Elimination (INFUSE)，VCC 是用 Variational Autoencoder 来选择更准确的伪标签，INFUSE 是一种数据修剪方法，在 SSL 下构建无标签样本的核心数据集。这些方法在多个数据集和设置中都非常有效，可以减少分类错误率并节省训练时间。VCC-INFUSE 在 CIFAR-100 数据集上将 FlexMatch 的错误率降低了 1.08%，同时节省了近一半的训练时间。

VCC-INFUSE: 半监督学习中准确高效的未标记样本选择

VCC-INFUSE: Towards Accurate and Efficient Selection of Unlabeled  Examples in Semi-supervised Learning

Universal lesion detection has great value for clinical practice as it aims
to detect various types of lesions in multiple organs on medical images. Deep
learning methods have shown promising results, but demanding large volumes of
annotated data for training. However, annotating medical images is costly and
requires specialized knowledge. The diverse forms and contrasts of objects in
medical images make fully annotation even more challenging, resulting in
incomplete annotations. Directly training ULD detectors on such datasets can
yield suboptimal results. Pseudo-label-based methods examine the training data
and mine unlabelled objects for retraining, which have shown to be effective to
tackle this issue. Presently, top-performing methods rely on a dynamic
label-mining mechanism, operating at the mini-batch level. However, the model's
performance varies at different iterations, leading to inconsistencies in the
quality of the mined labels and limits their performance enhancement. Inspired
by the observation that deep models learn concepts with increasing complexity,
we introduce an innovative exploratory training to assess the reliability of
mined lesions over time. Specifically, we introduce a teacher-student detection
model as basis, where the teacher's predictions are combined with incomplete
annotations to train the student. Additionally, we design a prediction bank to
record high-confidence predictions. Each sample is trained several times,
allowing us to get a sequence of records for each sample. If a prediction
consistently appears in the record sequence, it is likely to be a true object,
otherwise it may just a noise. This serves as a crucial criterion for selecting
reliable mined lesions for retraining. Our experimental results substantiate
that the proposed framework surpasses state-of-the-art methods on two medical
image datasets, demonstrating its superior performance.

通过引入创新的探索式训练方法，结合不完整注释和预测记录来选择可靠的矿化病灶进行重新训练，我们提出的框架在两个医学图像数据集上展现了卓越的性能，超越了现有最优方法。