Autism spectrum disorder (ASD) is a developmental condition that presents
significant challenges in social interaction, communication, and behavior.
Early intervention plays a pivotal role in enhancing cognitive abilities and
reducing autistic symptoms in children with ASD. Numerous clinical studies have
highlighted distinctive facial characteristics that distinguish ASD children
from typically developing (TD) children. In this study, we propose a practical
solution for ASD screening using facial images using YoloV8 model. By employing
YoloV8, a deep learning technique, on a dataset of Kaggle, we achieved
exceptional results. Our model achieved a remarkable 89.64% accuracy in
classification and an F1-score of 0.89. Our findings provide support for the
clinical observations regarding facial feature discrepancies between children
with ASD. The high F1-score obtained demonstrates the potential of deep
learning models in screening children with ASD. We conclude that the newest
version of YoloV8 which is usually used for object detection can be used for
classification problem of Austistic and Non-autistic images.

本研究使用 YoloV8 模型对 Kaggle 数据集中的面部图像进行分析，通过深度学习技术帮助自闭症儿童的诊断和分类，以提高早期干预和治疗效果。

使用深度学习方法筛选儿童自闭症谱系障碍：通过与其他模型进行比较来评估 YOLOv8 的分类模型

Screening Autism Spectrum Disorder in childrens using Deep Learning  Approach : Evaluating the classification model of YOLOv8 by comparing with  other models

We introduce MyStyle, a personalized deep generative prior trained with a few
shots of an individual. MyStyle allows to reconstruct, enhance and edit images
of a specific person, such that the output is faithful to the person's key
facial characteristics. Given a small reference set of portrait images of a
person (~100), we tune the weights of a pretrained StyleGAN face generator to
form a local, low-dimensional, personalized manifold in the latent space. We
show that this manifold constitutes a personalized region that spans latent
codes associated with diverse portrait images of the individual. Moreover, we
demonstrate that we obtain a personalized generative prior, and propose a
unified approach to apply it to various ill-posed image enhancement problems,
such as inpainting and super-resolution, as well as semantic editing. Using the
personalized generative prior we obtain outputs that exhibit high-fidelity to
the input images and are also faithful to the key facial characteristics of the
individual in the reference set. We demonstrate our method with fair-use images
of numerous widely recognizable individuals for whom we have the prior
knowledge for a qualitative evaluation of the expected outcome. We evaluate our
approach against few-shots baselines and show that our personalized prior,
quantitatively and qualitatively, outperforms state-of-the-art alternatives.

本文介绍了 MyStyle，这是一种使用少量照片训练的个性化深度生成先验的方法，其可以重建、增强和编辑特定人物的图像，生成的图像忠实于人物的关键面部特征。通过少量照片我们可以调整预训练的 StyleGAN 面部生成器的权重，形成一个本地的、低维的、个性化的流形空间，该流形空间包含与个人不同的各种肖像图片的潜在编码。此外，我们提出了一种统一的方法，将其应用于各种不适定的图像增强问题，例如修复、超分辨率和语义编辑，并取得了优异的定量和定性水平。

MyStyle: 个性化生成先验

MyStyle: A Personalized Generative Prior

In this paper, we aim to automatically render aging faces in a personalized
way. Basically, a set of age-group specific dictionaries are learned, where the
dictionary bases corresponding to the same index yet from different
dictionaries form a particular aging process pattern cross different age
groups, and a linear combination of these patterns expresses a particular
personalized aging process. Moreover, two factors are taken into consideration
in the dictionary learning process. First, beyond the aging dictionaries, each
subject may have extra personalized facial characteristics, e.g. mole, which
are invariant in the aging process. Second, it is challenging or even
impossible to collect faces of all age groups for a particular subject, yet
much easier and more practical to get face pairs from neighboring age groups.
Thus a personality-aware coupled reconstruction loss is utilized to learn the
dictionaries based on face pairs from neighboring age groups. Extensive
experiments well demonstrate the advantages of our proposed solution over other
state-of-the-arts in term of personalized aging progression, as well as the
performance gain for cross-age face verification by synthesizing aging faces.

该论文提出了一种自动化、个性化生成老化人脸的方法。利用一组特定年龄组的字典来学习老化过程，其中相同索引的字典基础表现为不同年龄组的老化特征，可线性组合生成个人的老化特征。在字典学习过程中，考虑每个受试者额外的个人化面部特征和采集领域的限制。该解决方案在个性化老化进程和跨年龄人脸验证合成老化人脸方面表现出优越的性能。