Big2Small: Learning from masked image modelling with heterogeneous self‐supervised knowledge distillation by Ziming Wang, Shumin Han, Xiaodi Wang, Jing Hao, Xianbin Cao, Baochang Zhang

“…Masked image modelling (MIM) methods achieve great success in various visual tasks but remain largely unexplored in knowledge distillation for heterogeneous deep models. …”
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Prior Knowledge-Informed Graph Neural Network with Multi-Source Data-Weighted Fusion for Intelligent Bogie Fault Diagnosis by Yuanxing Huang, Bofeng Cui, Xianqun Mao, Jinsong Yang

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Multi-species Fish Identification using Hybrid DeepCNN with Refined Squeeze and Excitation Architecture by Jansi Rani Sella Veluswami, Nivetha Panneerselvam

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Influence maximization under imbalanced heterogeneous networks via lightweight reinforcement learning with prior knowledge by Kehong You, Sanyang Liu, Yiguang Bai

“…Additionally, the node embedding vectors are input into a Deep Q Network (DQN) to commence the lightweight training process. …”
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RaDiT: A Differential Transformer-Based Hybrid Deep Learning Model for Radar Echo Extrapolation by Wenda Zhu, Zhenyu Lu, Yuan Zhang, Ziqi Zhao, Bingjian Lu, Ruiyi Li

“…Radar echo extrapolation, a critical spatiotemporal sequence forecasting task, requires precise modeling of motion trajectories and intensity evolution from sequential radar reflectivity inputs. Contemporary deep learning implementations face two operational limitations: progressive attenuation of predicted echo intensities during autoregressive inference and spectral leakage-induced diffusion at high-intensity echo boundaries. …”
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A Multi-Layer Attention Knowledge Tracking Method with Self-Supervised Noise Tolerance by Haifeng Wang, Hao Liu, Yanling Ge, Zhihao Yu

“…The knowledge tracing method based on deep learning is used to assess learners’ cognitive states, laying the foundation for personalized education. …”
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KG4Py: A toolkit for generating Python knowledge graph and code semantic search by Lu Liang, Yong Li, Ming Wen, Ying Liu

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Enhancing the Performance of YOLOv9t Through a Knowledge Distillation Approach for Real-Time Detection of Bloomed Damask Roses in the Field by Farhad Fatehi, Hossein Bagherpour, Jafar Amiri Parian

“…To address this challenge, knowledge distillation (KD) has emerged as a valuable model compression technique. …”
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Contrastive Learning Pre-Training and Quantum Theory for Cross-Lingual Aspect-Based Sentiment Analysis by Xun Li, Kun Zhang

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NMS-KSD: Efficient Knowledge Distillation for Dense Object Detection via Non-Maximum Suppression and Feature Storage by Suho Son, Byung Cheol Song

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Graph-Enhanced Deep Learning With Character Similarity Mining for Automated NOTAM Correction in Aviation Systems by Bing Dong, Haoran Yao, Chuang Luo, Ruichao Yang, Ziyue Wang

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Information extraction from green channel textual records on expressways using hybrid deep learning by Jiaona Chen, Jing Zhang, Weijun Tao, Yinli Jin, Heng Fan

“…We proposed a hybrid approach based on BIO labeling, pre-trained model, deep learning and CRF to build a named entity recognition (NER) model with the optimal prediction performance. …”
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Knowledge, Attitudes, and Concerns of Sudanese Doctors Regarding the Use of Artificial Intelligence Tools in Sudan by Taha Ibrahim Yousif, Amani Mohammed, Imad Brema

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Positive Anchor Area Merge Algorithm: A Knowledge Distillation Algorithm for Fruit Detection Tasks Based on Yolov8 by Xiangqun Shi, Xian Zhang, Yifan Su, Xun Zhang

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Optimal Knowledge Distillation through Non-Heuristic Control of Dark Knowledge by Darian Onchis, Codruta Istin, Ioan Samuila

“…In this paper, a method is introduced to control the dark knowledge values also known as soft targets, with the purpose of improving the training by knowledge distillation for multi-class classification tasks. …”
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Knowledge Improved Hybrid DNN–KAN Framework for Intrusion Detection in Wireless Sensor Networks by M. Sriraghavendra, Muna Elsadig, Ines Hilali Jaghdam, S. Abdel-Khalek, B. Galeebathullah, Salem Alkhalaf

“…This paper presents a Knowledge-Improved Hybrid Deep Neural Network-Kolmogorov Arnold Network (DNN-KAN) Framework for intrusion detection in WSNs, integrating data-driven learning with domain-specific knowledge to enhance detection performance. …”
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Empathetic Deep Learning: Transferring Adult Speech Emotion Models to Children With Gender-Specific Adaptations Using Neural Embeddings by Elina Lesyk, Tomás Arias-Vergara, Elmar Nöth, Andreas Maier, Juan Rafael Orozco-Arroyave, Paula Andrea Perez-Toro

“…Some of our findings indicate that female samples align more with high arousal emotions, while male samples align more with low arousal emotion, underscoring the importance of gender in emotion recognition. To the best of our knowledge, this is the first study in the field of deep learning applications on emotion recognition that analyses the effects of genders and age groups on emotional mapping.…”
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EEG-SKDNet: A Self-Knowledge Distillation Model With Scaled Weights for Emotion Recognition From EEG Signals by Thuong Duong Thi Mai, Duc-Quang Vu, Huy Nguyen Phuong, Trung-Nghia Phung

“…Unlike conventional knowledge distillation approaches that rely on large pre-trained teacher networks, our method leverages two different augmented views of the electroencephalography input, which are passed through a single student model to generate diverse predictions. …”
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Leveraging FastViT based knowledge distillation with EfficientNet-B0 for diabetic retinopathy severity classification by Jyotirmayee Rautaray, Ali B.M. Ali, Meenakshi Kandpal, Pranati Mishra, Rzgar Farooq Rashid, Farzona Alimova, Mohamed Kallel, Nadia Batool

“…Diabetic retinopathy (DR) remains a key contributor to eye impairment worldwide, requiring the development of efficient and accurate deep learning models for automated diagnosis. This study presents FastEffNet, a novel framework that leverages transformer-based knowledge distillation (KD) to enhance DR severity classification while reducing computational complexity. …”
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Multi-Class Brain Lesion Classification Using Deep Transfer Learning With MobileNetV3 by Ahmed Firas Majeed, Pedram Salehpour, Leili Farzinvash, Saeid Pashazadeh

“…Diagnosing brain tumors is challenging for radiologists because of the significant similarities between the tumor types. Deep learning models lack sufficient data to effectively learn the patterns of different tumors, leading adopting of transfer learning as a successful approach. …”
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