Unsupervised active learning has attracted increasing attention in recent
years, where its goal is to select representative samples in an unsupervised
setting for human annotating. Most existing works are based on shallow linear
models by assuming that each sample can be well approximated by the span (i.e.,
the set of all linear combinations) of certain selected samples, and then take
these selected samples as representative ones to label. However, in practice,
the data do not necessarily conform to linear models, and how to model
nonlinearity of data often becomes the key point to success. In this paper, we
present a novel Deep neural network framework for Unsupervised Active Learning,
called DUAL. DUAL can explicitly learn a nonlinear embedding to map each input
into a latent space through an encoder-decoder architecture, and introduce a
selection block to select representative samples in the the learnt latent
space. In the selection block, DUAL considers to simultaneously preserve the
whole input patterns as well as the cluster structure of data. Extensive
experiments are performed on six publicly available datasets, and experimental
results clearly demonstrate the efficacy of our method, compared with
state-of-the-arts.

本文提出了一种名为 DUAL 的新型深度神经网络框架来进行无监督主动学习，该框架可以通过编码器 - 解码器结构明确地学习非线性嵌入将每个输入映射到潜在空间，并引入选择块来在学习的潜在空间中选择代表性样本，实验结果表明 DUAL 与现有方法相比具有明显的优越性。

深度无监督主动学习

On Deep Unsupervised Active Learning

Facial action unit (AU) recognition is a crucial task for facial expressions
analysis and has attracted extensive attention in the field of artificial
intelligence and computer vision. Existing works have either focused on
designing or learning complex regional feature representations, or delved into
various types of AU relationship modeling. Albeit with varying degrees of
progress, it is still arduous for existing methods to handle complex
situations. In this paper, we investigate how to integrate the semantic
relationship propagation between AUs in a deep neural network framework to
enhance the feature representation of facial regions, and propose an AU
semantic relationship embedded representation learning (SRERL) framework.
Specifically, by analyzing the symbiosis and mutual exclusion of AUs in various
facial expressions, we organize the facial AUs in the form of structured
knowledge-graph and integrate a Gated Graph Neural Network (GGNN) in a
multi-scale CNN framework to propagate node information through the graph for
generating enhanced AU representation. As the learned feature involves both the
appearance characteristics and the AU relationship reasoning, the proposed
model is more robust and can cope with more challenging cases, e.g.,
illumination change and partial occlusion. Extensive experiments on the two
public benchmarks demonstrate that our method outperforms the previous work and
achieves state of the art performance.

本研究提出了基于知识图谱的面部动作单位识别建模框架，采用深层神经网络框架结合全局图神经网络进行语义关系传播，旨在增强面部区域特征提取提高面部表情识别性能，实验表明该方法在面部动作单位识别上取得了最佳表现。