Electronics, Journal Year: 2024, Volume and Issue: 13(22), P. 4530 - 4530
Published: Nov. 18, 2024
The rapid evolution of deep learning has led to significant achievements in computer vision, primarily driven by complex convolutional neural networks (CNNs). However, the increasing depth and parameter count these often result overfitting elevated computational demands. Knowledge distillation (KD) emerged as a promising technique address issues transferring knowledge from large, well-trained teacher model more compact student model. This paper introduces novel method that simplifies process narrows performance gap between models without relying on intricate representations. Our approach leverages unique network architecture designed enhance efficiency effectiveness transfer. Additionally, we introduce streamlined transfers effectively through simplified process, enabling achieve high accuracy with reduced Comprehensive experiments conducted CIFAR-10 dataset demonstrate our proposed achieves superior compared traditional KD methods established architectures such ResNet VGG networks. not only maintains but also significantly reduces training validation losses. Key findings highlight optimal hyperparameter settings (temperature T = 15.0 smoothing factor α 0.7), which yield highest lowest loss values. research contributes theoretical practical advancements distillation, providing robust framework for future applications compression optimization. simplicity pave way accessible scalable solutions deployment.
Language: Английский
Citations
10Signal Image and Video Processing, Journal Year: 2025, Volume and Issue: 19(2)
Published: Jan. 3, 2025
Language: Английский
Citations
0Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 108, P. 107964 - 107964
Published: April 19, 2025
Language: Английский
Citations
0Deleted Journal, Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 30, 2024
Brain tumor is a type of disease caused by uncontrolled cell proliferation in the brain leading to serious health issues such as memory loss and motor impairment. Therefore, early diagnosis tumors plays crucial role extend survival patients. However, given busy nature work radiologists aiming reduce likelihood false diagnoses, advancing technologies including computer-aided artificial intelligence have shown an important assisting radiologists. In recent years, number deep learning-based methods been applied for detection classification using MRI images achieved promising results. The main objective this paper present detailed review previous researches field. addition, This summarizes existing limitations significant highlights. study systematically reviews 60 articles published between 2020 January 2024, extensively covering transfer learning, autoencoders, transformers, attention mechanisms. key findings formulated provide analytic comparison future directions. aims comprehensive understanding automatic techniques that may be useful professionals academic communities working on detection.
Language: Английский
Citations
3Sensors, Journal Year: 2024, Volume and Issue: 24(22), P. 7189 - 7189
Published: Nov. 9, 2024
This study focuses on the classification of six different macrofungi species using advanced deep learning techniques. Fungi species, such as Amanita pantherina, Boletus edulis, Cantharellus cibarius, Lactarius deliciosus, Pleurotus ostreatus and Tricholoma terreum were chosen based their ecological importance distinct morphological characteristics. The research employed 5 machine techniques 12 models, including DenseNet121, MobileNetV2, ConvNeXt, EfficientNet, swin transformers, to evaluate performance in identifying fungi from images. DenseNet121 model demonstrated highest accuracy (92%) AUC score (95%), making it most effective distinguishing between species. also revealed that transformer-based particularly transformer, less effective, suggesting room for improvement application this task. Further advancements could be achieved by expanding datasets, incorporating additional data types biochemical, electron microscopy, RNA/DNA sequences, ensemble methods enhance performance. findings contribute valuable insights into both use biodiversity conservation
Language: Английский
Citations
3Sensing and Imaging, Journal Year: 2025, Volume and Issue: 26(1)
Published: April 16, 2025
Language: Английский
Citations
0Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown
Published: March 13, 2024
Language: Английский
Citations
1Published: June 21, 2024
Language: Английский
Citations
0