Zero-shot Learning (ZSL) classification categorizes or predicts classes
(labels) that are not included in the training set (unseen classes). Recent
works proposed different semantic autoencoder (SAE) models where the encoder
embeds a visual feature vector space into the semantic space and the decoder
reconstructs the original visual feature space. The objective is to learn the
embedding by leveraging a source data distribution, which can be applied
effectively to a different but related target data distribution. Such
embedding-based methods are prone to domain shift problems and are vulnerable
to biases. We propose an integral projection-based semantic autoencoder
(IP-SAE) where an encoder projects a visual feature space concatenated with the
semantic space into a latent representation space. We force the decoder to
reconstruct the visual-semantic data space. Due to this constraint, the
visual-semantic projection function preserves the discriminatory data included
inside the original visual feature space. The enriched projection forces a more
precise reconstitution of the visual feature space invariant to the domain
manifold. Consequently, the learned projection function is less domain-specific
and alleviates the domain shift problem. Our proposed IP-SAE model consolidates
a symmetric transformation function for embedding and projection, and thus, it
provides transparency for interpreting generative applications in ZSL.
Therefore, in addition to outperforming state-of-the-art methods considering
four benchmark datasets, our analytical approach allows us to investigate
distinct characteristics of generative-based methods in the unique context of
zero-shot inference.

提出了一种利用积分投影的语义自编码器（IP-SAE）模型，可以有效应用于不同但相关的目标数据分布，从而学习通过一个数据源分布进行嵌入。该模型能够解决因域漂移引起的问题和域变化的偏见，同时在四个基准数据集上表现出超越最新技术的表现。

一种基于积分投影的语义自编码器用于零样本学习

An Integral Projection-based Semantic Autoencoder for Zero-Shot Learning

Opinion summarization is the task of automatically generating summaries that
encapsulate information from multiple user reviews. We present Semantic
Autoencoder (SemAE) to perform extractive opinion summarization in an
unsupervised manner. SemAE uses dictionary learning to implicitly capture
semantic information from the review and learns a latent representation of each
sentence over semantic units. A semantic unit is supposed to capture an
abstract semantic concept. Our extractive summarization algorithm leverages the
representations to identify representative opinions among hundreds of reviews.
SemAE is also able to perform controllable summarization to generate
aspect-specific summaries. We report strong performance on SPACE and AMAZON
datasets, and perform experiments to investigate the functioning of our model.
Our code is publicly available at this https URL.

该研究提出了一种基于语义自动编码器（SemAE）的无监督抽取式网络评论摘要算法，并且能够进行具有可控性的方面特定性的摘要，实验结果表明该算法在 SPACE 和亚马逊数据集上表现强劲

基于稀疏编码的无监督抽取式观点摘要

Unsupervised Extractive Opinion Summarization Using Sparse Coding

Existing zero-shot learning (ZSL) models typically learn a projection
function from a feature space to a semantic embedding space (e.g.~attribute
space). However, such a projection function is only concerned with predicting
the training seen class semantic representation (e.g.~attribute prediction) or
classification. When applied to test data, which in the context of ZSL contains
different (unseen) classes without training data, a ZSL model typically suffers
from the project domain shift problem. In this work, we present a novel
solution to ZSL based on learning a Semantic AutoEncoder (SAE). Taking the
encoder-decoder paradigm, an encoder aims to project a visual feature vector
into the semantic space as in the existing ZSL models. However, the decoder
exerts an additional constraint, that is, the projection/code must be able to
reconstruct the original visual feature. We show that with this additional
reconstruction constraint, the learned projection function from the seen
classes is able to generalise better to the new unseen classes. Importantly,
the encoder and decoder are linear and symmetric which enable us to develop an
extremely efficient learning algorithm. Extensive experiments on six benchmark
datasets demonstrate that the proposed SAE outperforms significantly the
existing ZSL models with the additional benefit of lower computational cost.
Furthermore, when the SAE is applied to supervised clustering problem, it also
beats the state-of-the-art.

该论文提出一种基于语义自编码器的零样本学习方法，在编码器 - 解码器框架下，通过对编码 - 解码的自重建能力进行优化，从而使得模型更加泛化并可以在无训练数据的情况下预测新的未知类别。