Aspect Category Detection (ACD) aims to identify implicit and explicit
aspects in a given review sentence. The state-of-the-art approaches for ACD use
Deep Neural Networks (DNNs) to address the problem as a multi-label
classification task. However, learning category-specific representations
heavily rely on the amount of labeled examples, which may not readily available
in real-world scenarios. In this paper, we propose a novel approach to tackle
the ACD task by combining DNNs with Gradual Machine Learning (GML) in a
supervised setting. we aim to leverage the strength of DNN in semantic relation
modeling, which can facilitate effective knowledge transfer between labeled and
unlabeled instances during the gradual inference of GML. To achieve this, we
first analyze the learned latent space of the DNN to model the relations, i.e.,
similar or opposite, between instances. We then represent these relations as
binary features in a factor graph to efficiently convey knowledge. Finally, we
conduct a comparative study of our proposed solution on real benchmark datasets
and demonstrate that the GML approach, in collaboration with DNNs for feature
extraction, consistently outperforms pure DNN solutions.

将深度神经网络与渐进式机器学习相结合，以解决评论句子中隐含和显式方面的检测问题，通过分析学习得到的 DNN 的潜在空间来建模实例之间的相似或相反关系，并在因子图中表示这些关系作为二元特征，最终的比较研究表明，该方法与纯 DNN 解决方案相比始终表现更好。

监督逐步机器学习用于方面类别检测

Supervised Gradual Machine Learning for Aspect Category Detection

Few-shot image classification aims to accurately classify unlabeled images
using only a few labeled samples. The state-of-the-art solutions are built by
deep learning, which focuses on designing increasingly complex deep backbones.
Unfortunately, the task remains very challenging due to the difficulty of
transferring the knowledge learned in training classes to new ones. In this
paper, we propose a novel approach based on the non-i.i.d paradigm of gradual
machine learning (GML). It begins with only a few labeled observations, and
then gradually labels target images in the increasing order of hardness by
iterative factor inference in a factor graph. Specifically, our proposed
solution extracts indicative feature representations by deep backbones, and
then constructs both unary and binary factors based on the extracted features
to facilitate gradual learning. The unary factors are constructed based on
class center distance in an embedding space, while the binary factors are
constructed based on k-nearest neighborhood. We have empirically validated the
performance of the proposed approach on benchmark datasets by a comparative
study. Our extensive experiments demonstrate that the proposed approach can
improve the SOTA performance by 1-5% in terms of accuracy. More notably, it is
more robust than the existing deep models in that its performance can
consistently improve as the size of query set increases while the performance
of deep models remains essentially flat or even becomes worse.

基于渐进机器学习的非 i.i.d 范式，本文提出了一种新颖的途径，通过提取特征表示逐步学习，构建一元和二元因子来分类图像，实验证明该方法在准确性方面提高了 1-5%，并且相比于现有的深度模型更具鲁棒性。