Facial images disclose many hidden personal traits such as age, gender, race, health, emotion, and psychology. Understanding these traits will help to classify the people in different attributes. In this paper, we have presented a novel method for classifying images using a pretrained transformer model. We apply the pretrained transformer for the binary classification of facial images in criminal and non-criminal classes. The pretrained transformer of GPT-2 is trained to generate text and then fine-tuned to classify facial images. During the finetuning process with images, most of the layers of GT-2 are frozen during backpropagation and the model is frozen pretrained transformer (FPT). The FPT acts as a universal image classifier, and this paper shows the application of FPT on facial images. We also use our FPT on encrypted images for classification. Our FPT shows high accuracy on both raw facial images and encrypted images. We hypothesize the meta-learning capacity FPT gained because of its large size and trained on a large size with theory and experiments. The GPT-2 trained to generate a single word token at a time, through the autoregressive process, forced to heavy-tail distribution. Then the FPT uses the heavy-tail property as its meta-learning capacity for classifying images. Our work shows one way to avoid bias during the machine classification of images.The FPT encodes worldly knowledge because of the pretraining of one text, which it uses during the classification. The statistical error of classification is reduced because of the added context gained from the text.Our paper shows the ethical dimension of using encrypted data for classification.Criminal images are sensitive to share across the boundary but encrypted largely evades ethical concern.FPT showing good classification accuracy on encrypted images shows promise for further research on privacy-preserving machine learning.
Scalable Analytic Classifiers with Associative Drift Compensation for Class-Incremental Learning of Vision Transformers
School of Systems Science, Beijing Normal University, Beijing 100875, China（北京师范大学系统科学学院）；School of Artificial Intelligence, Anhui University, Hefei 230601, China（安徽大学人工智能学院）；Faculty of Informatics, Universita' Della Svizzera Italiana, Lugano, Switzerland（瑞士意大利大学信息学院）
Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer
University of Surrey（萨里大学）；iFlyTek-Surrey Joint Research Centre on Artificial Intelligence（科大讯飞-萨里人工智能联合研究中心）；School of Data Science, Fudan University（复旦大学数据科学学院）
Robustness of classifiers to universal perturbations: a geometric perspective
École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；ENS de Lyon（里昂高等师范学校）；CNRS（法国国家科学研究中心）；UCBL（里昂第一大学）；INRIA（法国国家信息与自动化研究所）；Université de Lyon（里昂大学）
Keypoint Counting Classifiers: Turning Vision Transformers into Self-Explainable Models Without Training
Department of Physics and Technology（物理与技术系）；UiT The Arctic University of Norway（北摩尔曼斯克大学）；Department of Applied Mathematics and Computer Science（应用数学与计算机科学系）；Technical University of Denmark（技术大学）
A Comparative Study of Gastric Histopathology Sub-size Image Classification: from Linear Regression to Visual Transformer
Northeastern University（东北大学）；Cancer Hospital, China Medical University（中国医科大学肿瘤医院）；Liaoning Cancer Hospital and Institute（辽宁省肿瘤医院暨研究所）；Shengjing Hospital, China Medical University（中国医科大学附属盛京医院）；University of Luebeck（吕贝克大学）
Normal and Atypical Mitosis Image Classifier using Efficient Vision Transformer
Laboratory of Cancer Biology and Genetics, NCI, NIH, Bethesda, MD, 20852, USA（癌症生物学与遗传学实验室，国家癌症研究所，美国国家卫生研究院，贝塞斯达，马里兰州，20852，美国）；Department of Radiation Oncology, Duke University Medical Center, Durham, NC, 27705, USA（放射肿瘤学系，杜克大学医学中心，达勒姆，北卡罗来纳州，27705，美国）；University of Hawaii Cancer Center, Honolulu, HI, 96813, USA（夏威夷大学癌症中心，霍洛汉，夏威夷州，96813，美国）
Universal Approximation of Visual Autoregressive Transformers
The University of Chicago（芝加哥大学）；University of New South Wales（新南威尔士大学）；The University of Hong Kong（香港大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；The Simons Institute for the Theory of Computing（西蒙斯理论计算研究所）
COCA: Classifier-Oriented Calibration via Textual Prototype for Source-Free Universal Domain Adaptation
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Prompt-based Grouping Transformer for Nucleus Detection and Classification
Sun Yat-sen University（中山大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Guangdong University of Technology（广东工业大学）
DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecture
School of Data Science, University of Science and Technology of China（中国科学技术大学数据科学学院）；School of Management, University of Science and Technology of China（中国科学技术大学管理学院）；School of Population Health, UNSW Sydney（新南威尔士大学悉尼分校人口健康学院）；Genmab Inc.（杰特贝公司）
Universal Polarization Transformations: Spatial programming of polarization scattering matrices using a deep learning-designed diffractive polarization transformer
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；California NanoSystems Institute (CNSI)（加州纳米系统研究所）
