In this paper, we address an open problem of zero-shot learning. Its
principle is based on learning a mapping that associates feature vectors
extracted from i.e. images and attribute vectors that describe objects and/or
scenes of interest. In turns, this allows classifying unseen object classes
and/or scenes by matching feature vectors via mapping to a newly defined
attribute vector describing a new class. Due to importance of such a learning
task, there exist many methods that learn semantic, probabilistic, linear or
piece-wise linear mappings. In contrast, we apply well-established kernel
methods to learn a non-linear mapping between the feature and attribute spaces.
We propose an easy learning objective inspired by the Linear Discriminant
Analysis, Kernel-Target Alignment and Kernel Polarization methods that promotes
incoherence. We evaluate performance of our algorithm on the Polynomial as well
as shift-invariant Gaussian and Cauchy kernels. Despite simplicity of our
approach, we obtain state-of-the-art results on several zero-shot learning
datasets and benchmarks including a recent AWA2 dataset.

本研究在零样本学习中使用核方法来学习特征和属性空间之间的非线性映射，提出了一种简单的学习目标，并在多个零样本学习数据集和基准测试中取得了最优结果。

零样本核学习

Zero-Shot Kernel Learning

Zero shot learning in Image Classification refers to the setting where images
from some novel classes are absent in the training data but other information
such as natural language descriptions or attribute vectors of the classes are
available. This setting is important in the real world since one may not be
able to obtain images of all the possible classes at training. While previous
approaches have tried to model the relationship between the class attribute
space and the image space via some kind of a transfer function in order to
model the image space correspondingly to an unseen class, we take a different
approach and try to generate the samples from the given attributes, using a
conditional variational autoencoder, and use the generated samples for
classification of the unseen classes. By extensive testing on four benchmark
datasets, we show that our model outperforms the state of the art, particularly
in the more realistic generalized setting, where the training classes can also
appear at the test time along with the novel classes.

这篇文章研究了零样本学习在图像分类中的应用，提出了一种基于条件变分自编码器的生成模型方法，用于模拟未见过的样本，获得更好的分类性能。

一种使用条件变分自编码器进行零样本学习的生成模型

A Generative Model For Zero Shot Learning Using Conditional Variational  Autoencoders

We introduce several techniques for sampling and visualizing the latent
spaces of generative models. Replacing linear interpolation with spherical
linear interpolation prevents diverging from a model's prior distribution and
produces sharper samples. J-Diagrams and MINE grids are introduced as
visualizations of manifolds created by analogies and nearest neighbors. We
demonstrate two new techniques for deriving attribute vectors: bias-corrected
vectors with data replication and synthetic vectors with data augmentation.
Binary classification using attribute vectors is presented as a technique
supporting quantitative analysis of the latent space. Most techniques are
intended to be independent of model type and examples are shown on both
Variational Autoencoders and Generative Adversarial Networks.

介绍了几种用于采样和可视化生成模型的潜在空间的技术，包括通过球面线性插值代替线性插值以防止背离模型先验分布并生成更清晰的样本，使用 J-Diagrams 和 MINE grids 可视化类比和最近邻创造的流形，提出使用数据复制的偏差校正向量和使用数据增广的合成向量来推导属性向量的两种新技术，提出使用属性向量进行二元分类以支持潜在空间的定量分析，这些技术大多数都是独立于模型类型的，演示了变分自编码器和生成对抗网络的示例。

生成网络的采样

Sampling Generative Networks

In this paper we propose a general framework for learning distributed
representations of attributes: characteristics of text whose representations
can be jointly learned with word embeddings. Attributes can correspond to
document indicators (to learn sentence vectors), language indicators (to learn
distributed language representations), meta-data and side information (such as
the age, gender and industry of a blogger) or representations of authors. We
describe a third-order model where word context and attribute vectors interact
multiplicatively to predict the next word in a sequence. This leads to the
notion of conditional word similarity: how meanings of words change when
conditioned on different attributes. We perform several experimental tasks
including sentiment classification, cross-lingual document classification, and
blog authorship attribution. We also qualitatively evaluate conditional word
neighbours and attribute-conditioned text generation.

通过在单词嵌入中联合学习文本特征的分布式表征的一般框架，我们提出了一种第三阶段模型，其中单词环境和属性向量相互作用以预测序列中的下一个单词，并通过情感分类、跨语言文本分类和博客作者归因等多个实验任务进行了评估。