The recent success of deep learning is mostly due to the availability of big
datasets with clean annotations. However, gathering a cleanly annotated dataset
is not always feasible due to practical challenges. As a result, label noise is
a common problem in datasets, and numerous methods to train deep neural
networks in the presence of noisy labels are proposed in the literature. These
methods commonly use benchmark datasets with synthetic label noise on the
training set. However, there are multiple types of label noise, and each of
them has its own characteristic impact on learning. Since each work generates a
different kind of label noise, it is problematic to test and compare those
algorithms in the literature fairly. In this work, we provide a detailed
analysis of the effects of different kinds of label noise on learning.
Moreover, we propose a generic framework to generate feature-dependent label
noise, which we show to be the most challenging case for learning. Our proposed
method aims to emphasize similarities among data instances by sparsely
distributing them in the feature domain. By this approach, samples that are
more likely to be mislabeled are detected from their softmax probabilities, and
their labels are flipped to the corresponding class. The proposed method can be
applied to any clean dataset to synthesize feature-dependent noisy labels. For
the ease of other researchers to test their algorithms with noisy labels, we
share corrupted labels for the most commonly used benchmark datasets. Our code
and generated noisy synthetic labels are available online.

本研究分析了不同类型的标签噪声对深度学习的影响，提出了一种基于特征相关性的标签噪声生成方法，并提供了常用基准数据集的标签噪声以便其他研究人员测试其算法。

标签噪声类型及其对深度学习的影响

Label Noise Types and Their Effects on Deep Learning

Designing a logo for a new brand is a lengthy and tedious back-and-forth
process between a designer and a client. In this paper we explore to what
extent machine learning can solve the creative task of the designer. For this,
we build a dataset -- LLD -- of 600k+ logos crawled from the world wide web.
Training Generative Adversarial Networks (GANs) for logo synthesis on such
multi-modal data is not straightforward and results in mode collapse for some
state-of-the-art methods. We propose the use of synthetic labels obtained
through clustering to disentangle and stabilize GAN training. We are able to
generate a high diversity of plausible logos and we demonstrate latent space
exploration techniques to ease the logo design task in an interactive manner.
Moreover, we validate the proposed clustered GAN training on CIFAR 10,
achieving state-of-the-art Inception scores when using synthetic labels
obtained via clustering the features of an ImageNet classifier. GANs can cope
with multi-modal data by means of synthetic labels achieved through clustering,
and our results show the creative potential of such techniques for logo
synthesis and manipulation. Our dataset and models will be made publicly
available at this https URL

本文探讨机器学习在设计商标方面的创造性任务中的应用，利用聚类获得的合成标签，对多模态数据的 GAN 训练进行稳定，并展示潜在空间探索技术，以交互方式简化商标设计任务。