The scarcity of labeled data is a critical obstacle to deep learning.
Semi-supervised learning (SSL) provides a promising way to leverage unlabeled
data by pseudo labels. However, when the size of labeled data is very small
(say a few labeled samples per class), SSL performs poorly and unstably,
possibly due to the low quality of learned pseudo labels. In this paper, we
propose a new SSL method called DP-SSL that adopts an innovative data
programming (DP) scheme to generate probabilistic labels for unlabeled data.
Different from existing DP methods that rely on human experts to provide
initial labeling functions (LFs), we develop a multiple-choice learning~(MCL)
based approach to automatically generate LFs from scratch in SSL style. With
the noisy labels produced by the LFs, we design a label model to resolve the
conflict and overlap among the noisy labels, and finally infer probabilistic
labels for unlabeled samples. Extensive experiments on four standard SSL
benchmarks show that DP-SSL can provide reliable labels for unlabeled data and
achieve better classification performance on test sets than existing SSL
methods, especially when only a small number of labeled samples are available.
Concretely, for CIFAR-10 with only 40 labeled samples, DP-SSL achieves 93.82%
annotation accuracy on unlabeled data and 93.46% classification accuracy on
test data, which are higher than the SOTA results.

本文提出了一种新的半监督学习方法 DP-SSL，采用创新的数据编程（DP）方案为无标签数据生成概率标签，通过自动生成标签函数并解决不同标签之间的冲突，成功缓解了有限标签数据的问题，实验证明 DP-SSL 可以为无标签数据提供可靠标签和更好的分类性能。

DP-SSL: 面向用少量标注样本提升鲁棒性的半监督学习

DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled  Samples

The availability of labelled data is one of the main limitations in machine
learning. We can alleviate this using weak supervision: a framework that uses
expert-defined rules $\boldsymbol{\lambda}$ to estimate probabilistic labels
$p(y|\boldsymbol{\lambda})$ for the entire data set. These rules, however, are
dependent on what experts know about the problem, and hence may be inaccurate
or may fail to capture important parts of the problem-space. To mitigate this,
we propose Active WeaSuL: an approach that incorporates active learning into
weak supervision. In Active WeaSuL, experts do not only define rules, but they
also iteratively provide the true label for a small set of points where the
weak supervision model is most likely to be mistaken, which are then used to
better estimate the probabilistic labels. In this way, the weak labels provide
a warm start, which active learning then improves upon. We make two
contributions: 1) a modification of the weak supervision loss function, such
that the expert-labelled data inform and improve the combination of weak
labels; and 2) the maxKL divergence sampling strategy, which determines for
which data points expert labelling is most beneficial. Our experiments show
that when the budget for labelling data is limited (e.g. $\leq 60$ data
points), Active WeaSuL outperforms weak supervision, active learning, and
competing strategies, with only a handful of labelled data points. This makes
Active WeaSuL ideal for situations where obtaining labelled data is difficult.

本文介绍了一种新的机器学习辅助标注方法，即 Active WeaSuL。该方法使用专家定义的规则来估算整个数据集的概率标签，并在弱监督模型容易出错的几个点上迭代提供真实标签以优化模型性能。实验证明，Active WeaSuL 比其他方法更适用于获取标记数据困难的情况下。

主动学习提升弱监督的 WeaSuL 算法

Active WeaSuL: Improving Weak Supervision with Active Learning

Despite the availability of a huge amount of video data accompanied by
descriptive texts, it is not always easy to exploit the information contained
in natural language in order to automatically recognize video concepts. Towards
this goal, in this paper we use textual cues as means of supervision,
introducing two weakly supervised techniques that extend the Multiple Instance
Learning (MIL) framework: the Fuzzy Sets Multiple Instance Learning (FSMIL) and
the Probabilistic Labels Multiple Instance Learning (PLMIL). The former encodes
the spatio-temporal imprecision of the linguistic descriptions with Fuzzy Sets,
while the latter models different interpretations of each description's
semantics with Probabilistic Labels, both formulated through a convex
optimization algorithm. In addition, we provide a novel technique to extract
weak labels in the presence of complex semantics, that consists of semantic
similarity computations. We evaluate our methods on two distinct problems,
namely face and action recognition, in the challenging and realistic setting of
movies accompanied by their screenplays, contained in the COGNIMUSE database.
We show that, on both tasks, our method considerably outperforms a
state-of-the-art weakly supervised approach, as well as other baselines.

本文介绍了两种基于多示例学习框架的弱监督技术，即模糊集多示例学习和概率标签多实例学习，旨在利用文本线索自动识别视频概念，同时提供一种提取复杂语义中的弱标签的新方法。作者在 COGNIMUSE 数据库中展示了这些方法在面部和动作识别任务上的较强性能。