Object classes that surround us have a natural tendency to emerge at varying
levels of abstraction. We propose a Bayesian approach to zero-shot learning
(ZSL) that introduces the notion of meta-classes and implements a Bayesian
hierarchy around these classes to effectively blend data likelihood with local
and global priors. Local priors driven by data from seen classes, i.e. classes
that are available at training time, become instrumental in recovering unseen
classes, i.e. classes that are missing at training time, in a generalized ZSL
setting. Hyperparameters of the Bayesian model offer a convenient way to
optimize the trade-off between seen and unseen class accuracy in addition to
guiding other aspects of model fitting. We conduct experiments on seven
benchmark datasets including the large scale ImageNet and show that our model
improves the current state of the art in the challenging generalized ZSL
setting.

本文提出了一种贝叶斯方法，通过引入元类概念和在类周围实现贝叶斯层次结构，将数据似然性与本地和全局先验相结合，从而在归纳式零样本学习中恢复未见过的类。

贝叶斯零样本学习

Bayesian Zero-Shot Learning

Zero-shot learning (ZSL) enables solving a task without the need to see its
examples. In this paper, we propose two ZSL frameworks that learn to synthesize
parameters for novel unseen classes. First, we propose to cast the problem of
ZSL as learning manifold embeddings from graphs composed of object classes,
leading to a flexible approach that synthesizes "classifiers" for the unseen
classes. Then, we define an auxiliary task of synthesizing "exemplars" for the
unseen classes to be used as an automatic denoising mechanism for any existing
ZSL approaches or as an effective ZSL model by itself. On five visual
recognition benchmark datasets, we demonstrate the superior performances of our
proposed frameworks in various scenarios of both conventional and generalized
ZSL. Finally, we provide valuable insights through a series of empirical
analyses, among which are a comparison of semantic representations on the full
ImageNet benchmark as well as a comparison of metrics used in generalized ZSL.
Our code and data are publicly available at
this https URL

本文提出了两个使用零样本学习（ZSL）系统，该系统使用流形嵌入和自动生成视觉分类器来解决具有未知类的任务，并且在五个类视觉识别基准数据集上表现出卓越的性能。