Deep Transfer Learning for Biology Cross-Domain Image Classification by Chunfeng Guo, Bin Wei, Kun Yu

“…According to the literature, previous studies mainly focus on transferring from ImageNet to a specific domain or transferring between two closely related domains. …”
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A human single-neuron dataset for object recognition by Runnan Cao, Peter Brunner, Nicholas J. Brandmeir, Jon T. Willie, Shuo Wang

“…We employed two sets of naturalistic object images from leading datasets extensively used in primate neural recordings and computer vision models: we recorded 1204 neurons using the ImageNet stimuli, which included broader object categories (10 different images per category for 50 categories), and we recorded 512 neurons using the Microsoft COCO stimuli, which featured a higher number of images per category (50 different images per category for 10 categories). …”
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A comprehensive evaluation of histopathology foundation models for ovarian cancer subtype classification by Jack Breen, Katie Allen, Kieran Zucker, Lucy Godson, Nicolas M. Orsi, Nishant Ravikumar

“…Attention-based multiple instance learning classifiers were compared using three ImageNet-pretrained encoders and fourteen foundation models, each trained with 1864 whole slide images and validated through hold-out testing and two external validations (the Transcanadian Study and OCEAN Challenge). …”
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Avoiding catastrophic overfitting in fast adversarial training with adaptive similarity step size. by Jie-Chao Zhao, Jin Ding, Yong-Zhi Sun, Ping Tan, Ji-En Ma, You-Tong Fang

“…We conduct various adversarial attack tests on ResNet18 and VGG19 models using the CIFAR-10, CIFAR-100 and Tiny ImageNet datasets. The experimental results demonstrate that our method effectively avoids catastrophic overfitting. …”
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Convolutional neural network prediction of the particle size distribution of soil from close-range images by Enrico Soranzo, Carlotta Guardiani, Wei Wu

“…We employed transfer learning by using MobileNet pre-trained on ImageNet and adding additional layers to fine-tune the model for our specific task. …”
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Class Activation Map Guided Backpropagation for Discriminative Explanations by Yongjie Liu, Wei Guo, Xudong Lu, Lanju Kong, Zhongmin Yan

“…CGBP leverages class activation maps during backpropagation to enhance gradient selection, achieving consistent improvements across four models (VGG16, VGG19, ResNet50, and ResNet101) on ImageNet’s validation set. Notably, on VGG16, CGBP improves SIC, AIC, and IS scores by 10.3%, 11.5%, and 4.5%, respectively, compared to RectGrad while maintaining competitive DS performance. …”
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Brain tumor segmentation by deep learning transfer methods using MRI images by E.Y. Shchetinin

“…It is proposed to use deep convolutional neural network models pre-trained on the ImageNet dataset as U-Net encoders. Among such models, VGG16, VGG19, Mobilenetv2, Inception, Efficientnetb7, InceptionResnetV2, DenseNet201, DenseNet121 were used. …”
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Disease Identification of Lentinus Edodes Sticks Based on Deep Learning Model by Dawei Zu, Feng Zhang, Qiulan Wu, Wenyan Wang, Zimeng Yang, Zhengpeng Hu

“…Second, based on the ResNeXt-50(32 × 4d) model and the pretraining weight of the ImageNet dataset, the influence of pretraining weight parameters on recognition accuracy was studied. …”
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Fault diagnosis of rotating parts integrating transfer learning and ConvNeXt model by Zhikai Xing, Yongbao Liu, Qiang Wang, Junqiang Fu

“…The fault diagnosis process leverages a pre-trained ConvNeXt model, initially trained on the ImageNet dataset, and fine-tunes its parameters using the synthesized RGB images to perform the fault classification task. …”
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CoLR: Classification-Oriented Local Representation for Image Recognition by Tan Guo, Lei Zhang, Xiaoheng Tan, Liu Yang, Zhiwei Guo, Fupeng Wei

“…Specifically, the deep features of the object dataset are obtained by a well-trained convolutional neural network (CNN) with five convolutional layers and three fully connected layers on the challenging ImageNet. Extensive experiments verify the superiority of CoLR in comparison with some state-of-the-art models.…”
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APDL: an adaptive step size method for white-box adversarial attacks by Jiale Hu, Xiang Li, Changzheng Liu, Ronghua Zhang, Junwei Tang, Yi Sun, Yuedong Wang

“…Experiments conducted on ImageNet-compatible datasets demonstrate that APDL is significantly faster than the commonly used nonadaptive methods, whereas the TDLF method exhibits strong transferability.…”
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ECG-based transfer learning for cardiovascular disease: A scoping review by Sharifah Noor Masidayu Sayed Ismail, Siti Fatimah Abdul Razak, Nor Azlina Ab Aziz

“…Pre-trained models such as ResNet, AlexNet, and VGG, which are trained on ImageNet, are often used with two-dimensional (2D) ECG data as network input, achieving accuracy rates exceeding 90 %. …”
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Research on Road Adhesion Condition Identification Based on an Improved ALexNet Model by QiMing Wang, JinMing Xu, Tao Sun, ZhiChao Lv, GaoQiang Zong

“…First, the ALexNet network model is pretrained on the ImageNet dataset offline. Second, the weights of the shallow network structure after training, including the convolutional layer, are saved and migrated to the proposed model. …”
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The Use of Machine Learning to Support the Diagnosis of Oral Alterations by Rosana Leal do Prado, Juliane Avansini Marsicano, Amanda Keren Frois, Jacques Duílio Brancher

“…The CNNs were implemented using the Keras library, trained with pre-trained ImageNet weights, and evaluated using accuracy and AUC metrics. …”
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An Adversarial Attack via Penalty Method by Jiyuan Sun, Haibo Yu, Jianjun Zhao

“…Extensive experiments on three test benches (MNIST, CIFAR10, and ImageNet) demonstrate that compared with existing methods, our attack can generate adversarial examples with minor perturbations at a higher success rate. …”
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A deep learning-driven multi-layered steganographic approach for enhanced data security by Yousef Sanjalawe, Salam Al-E’mari, Salam Fraihat, Mosleh Abualhaj, Emran Alzubi

“…Extensive evaluations using benchmark datasets, including Tiny ImageNet, COCO, and CelebA, demonstrate the approach’s superior performance. …”
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Mape: defending against transferable adversarial attacks using multi-source adversarial perturbations elimination by Xinlei Liu, Jichao Xie, Tao Hu, Peng Yi, Yuxiang Hu, Shumin Huo, Zhen Zhang

“…In a black-box attack scenario utilizing ResNet-34 as the target model, our approach achieves average defense rates of over 95.1% on CIFAR-10 and over 71.5% on Mini-ImageNet, demonstrating state-of-the-art performance.…”
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Tailored Channel Pruning: Achieve Targeted Model Complexity Through Adaptive Sparsity Regularization by Suwoong Lee, Yunho Jeon, Seungjae Lee, Junmo Kim

“…Through various experiments on the CIFAR-10 and ImageNet datasets, we demonstrate the effectiveness of the proposed method and achieve state-of-the-art accuracy after pruning.…”
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Improving Performance of Real-Time Object Detection in Edge Device Through Concurrent Multi-Frame Processing by Seunghwan Kim, Changjong Kim, Sunggon Kim

“…We implement our scheme in YOLO (You Only Look Once), one of the most popular real-time object detection algorithms, on a state-of-the-art, resource-constrained IoT edge device, Nvidia Jetson Orin Nano, using real-world video and image datasets, including MS-COCO, ImageNet, PascalVOC, DOTA, animal videos, and car-traffic videos. …”
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DM-KD: Decoupling Mixed-Images for Efficient Knowledge Distillation by Jongkyung Im, Younho Jang, Junpyo Lim, Taegoo Kang, Chaoning Zhang, Sung-Ho Bae

“…To verify the effectiveness of the proposed method, we experiment on various datasets and mixed augmentation methods, demonstrating that the proposed method showed 0.31%-0.69% improvement in top-1 accuracy compared to the original KD method on theImageNet dataset.…”
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