Generalized zero-shot learning models (GZSL) aim to recognize samples from
seen or unseen classes using only samples from seen classes as training data.
During inference, GZSL methods are often biased towards seen classes due to the
visibility of seen class samples during training. Most current GZSL methods try
to learn an accurate projection function (from visual space to semantic space)
to avoid bias and ensure the effectiveness of GZSL methods. However, during
inference, the computation of distance will be important when we classify the
projection of any sample into its nearest class since we may learn a biased
projection function in the model. In our work, we attempt to learn a
parameterized Mahalanobis distance within the framework of VAEGAN (Variational
Autoencoder \& Generative Adversarial Networks), where the weight matrix
depends on the network's output. In particular, we improved the network
structure of VAEGAN to leverage the discriminative models of two branches to
separately predict the seen samples and the unseen samples generated by this
seen one. We proposed a new loss function with two branches to help us learn
the optimized Mahalanobis distance representation. Comprehensive evaluation
benchmarks on four datasets demonstrate the superiority of our method over the
state-of-the-art counterparts. Our codes are available at
this https URL

通过在 VAEGAN 框架中学习带权重矩阵的参数化马氏距离，我们提出了一种改进的网络结构来优化广义零样本学习模型，通过两个分支的鉴别模型分别预测已知样本和由已知样本生成的未知样本，实验结果显示我们的方法在四个数据集上优于现有最先进的方法。

广义零样本学习中的投影偏差度量学习

Metric Learning for Projections Bias of Generalized Zero-shot Learning

Zero-shot learning (ZSL) is made possible by learning a projection function
between a feature space and a semantic space (e.g.,~an attribute space). Key to
ZSL is thus to learn a projection that is robust against the often large domain
gap between the seen and unseen class domains. In this work, this is achieved
by unseen class data synthesis and robust projection function learning.
Specifically, a novel semantic data synthesis strategy is proposed, by which
semantic class prototypes (e.g., attribute vectors) are used to simply perturb
seen class data for generating unseen class ones. As in any data
synthesis/hallucination approach, there are ambiguities and uncertainties on
how well the synthesised data can capture the targeted unseen class data
distribution. To cope with this, the second contribution of this work is a
novel projection learning model termed competitive bidirectional projection
learning (BPL) designed to best utilise the ambiguous synthesised data.
Specifically, we assume that each synthesised data point can belong to any
unseen class; and the most likely two class candidates are exploited to learn a
robust projection function in a competitive fashion. As a third contribution,
we show that the proposed ZSL model can be easily extended to few-shot learning
(FSL) by again exploiting semantic (class prototype guided) feature synthesis
and competitive BPL. Extensive experiments show that our model achieves the
state-of-the-art results on both problems.

本研究提出了一种基于数据合成和竞争双向投影学习的零样本和少样本学习模型，在语义空间和特征空间之间学习了一个鲁棒的投影函数，并将它应用于语义数据的合成和模糊的非监督训练中，取得了最先进的结果。

基于语义特征合成和竞争学习的零次和少次学习

Zero and Few Shot Learning with Semantic Feature Synthesis and  Competitive Learning

Zero-shot learning (ZSL) aims to recognize unseen object classes without any
training samples, which can be regarded as a form of transfer learning from
seen classes to unseen ones. This is made possible by learning a projection
between a feature space and a semantic space (e.g. attribute space). Key to ZSL
is thus to learn a projection function that is robust against the often large
domain gap between the seen and unseen classes. In this paper, we propose a
novel ZSL model termed domain-invariant projection learning (DIPL). Our model
has two novel components: (1) A domain-invariant feature self-reconstruction
task is introduced to the seen/unseen class data, resulting in a simple linear
formulation that casts ZSL into a min-min optimization problem. Solving the
problem is non-trivial, and a novel iterative algorithm is formulated as the
solver, with rigorous theoretic algorithm analysis provided. (2) To further
align the two domains via the learned projection, shared semantic structure
among seen and unseen classes is explored via forming superclasses in the
semantic space. Extensive experiments show that our model outperforms the
state-of-the-art alternatives by significant margins.

本文提出一种新的零样本学习模型（DIPL），其中引入一种域不变特征自重建任务并通过语义空间中超类的形成进一步对齐两个域，以解决转移学习中看不见的物体分类问题。我们的模型在大量试验中表现出优异的性能，优于现有技术方案。