Collecting large training datasets, annotated with high-quality labels, is
costly and time-consuming. This paper proposes a novel framework for training
deep convolutional neural networks from noisy labeled datasets that can be
obtained cheaply. The problem is formulated using an undirected graphical model
that represents the relationship between noisy and clean labels, trained in a
semi-supervised setting. In our formulation, the inference over latent clean
labels is tractable and is regularized during training using auxiliary sources
of information. The proposed model is applied to the image labeling problem and
is shown to be effective in labeling unseen images as well as reducing label
noise in training on CIFAR-10 and MS COCO datasets.

该论文提出了一种基于无标签干扰数据集训练深度卷积神经网络的新型框架，并使用一个无向图模型来描述干净和嘈杂标签之间的关系，在监督学习过程中学习这个模型。该模型在图像标注问题上应用，并在 CIFAR-10 和 MS COCO 数据集上展示出有效的标注效果和在训练中实现了减少标签噪声的效果。

训练深度判别神经网络对抗标签噪声的鲁棒性

Toward Robustness against Label Noise in Training Deep Discriminative  Neural Networks

Current systems of fine-grained entity typing use distant supervision in
conjunction with existing knowledge bases to assign categories (type labels) to
entity mentions. However, the type labels so obtained from knowledge bases are
often noisy (i.e., incorrect for the entity mention's local context). We define
a new task, Label Noise Reduction in Entity Typing (LNR), to be the automatic
identification of correct type labels (type-paths) for training examples, given
the set of candidate type labels obtained by distant supervision with a given
type hierarchy. The unknown type labels for individual entity mentions and the
semantic similarity between entity types pose unique challenges for solving the
LNR task. We propose a general framework, called PLE, to jointly embed entity
mentions, text features and entity types into the same low-dimensional space
where, in that space, objects whose types are semantically close have similar
representations. Then we estimate the type-path for each training example in a
top-down manner using the learned embeddings. We formulate a global objective
for learning the embeddings from text corpora and knowledge bases, which adopts
a novel margin-based loss that is robust to noisy labels and faithfully models
type correlation derived from knowledge bases. Our experiments on three public
typing datasets demonstrate the effectiveness and robustness of PLE, with an
average of 25% improvement in accuracy compared to next best method.

使用 PLE 的低维度空间嵌入实体提及、文本特征和实体类型，以自动消除知识库标签中的错误，并为每个训练实例估计其类型路径。