Effect of transfer learning on the performance of VGGNet-16 and ResNet-50 for the classification of organic and residual waste

Wu, Fangfang; Lin, Hui

doi:10.3389/fenvs.2022.1043843

Cited by 7 publications

(4 citation statements)

References 34 publications

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“…The models, MTED-TL and MUDE-CNN were trained using two different strategies: one involved sequential training of individual branches, while the other trained all branches simultaneously. The model utilizing transfer learning demonstrated greater efficiency, in line with existing literature and highlighting the potential for transfer learning to reduce computational time [117,118]. Another key factor contributing to this phenomenon is MTED-TL's individualized branch training approach.…”

Section: Temporal-3d Stress Field Predictions By Mude-cnn and Mted-tlsupporting

confidence: 74%

Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

Rezasefat,

Hogan

2024

Mach. Learn.: Sci. Technol.

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This study investigates the application of machine learning models to predict time-evolving stress fields in complex three-dimensional structures trained with full-scale finite element simulation data. Two novel architectures, the Multi-Decoder CNN (MUDE-CNN) and the Multiple Encoder-Decoder Model with Transfer Learning (MTED-TL), were introduced to address the challenge of predicting the progressive and spatial evolutional of stress distributions around defects. The MUDE-CNN leveraged a shared encoder for simultaneous feature extraction and employed multiple decoders for distinct time frame predictions, while MTED-TL progressively transferred knowledge from one encoder-decoder block to another, thereby enhancing prediction accuracy through transfer learning. These models were evaluated to assess their accuracy, with a particular focus on predicting temporal stress fields around an additive manufacturing-induced isolated pore, as understanding such defects is crucial for assessing mechanical properties and structural integrity in materials and components fabricated via additive manufacturing. The temporal model evaluation demonstrated MTED-TL's consistent superiority over MUDE-CNN, owing to transfer learning's advantageous initialization of weights and smooth loss curves. Furthermore, an autoregressive training framework was introduced to improve temporal predictions, consistently outperforming both MUDE-CNN and MTED-TL. By accurately predicting temporal stress fields around AM-induced defects, these models can enable real-time monitoring and proactive defect mitigation during the fabrication process. This capability ensures enhanced component quality and enhances the overall reliability of additively manufactured parts.

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Section: Temporal-3d Stress Field Predictions By Mude-cnn and Mted-tlsupporting

confidence: 74%

Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

Rezasefat,

Hogan

2024

Mach. Learn.: Sci. Technol.

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“…Transfer learning emerged as a pivotal component of this study, offering several advantages. By leveraging pre-trained EfficientNet models originally trained on the ImageNet dataset, the study circumvented the time-consuming process of training models from scratch [55]. This expedited the learning process while maintaining a high level of accuracy.…”

Section: Discussionmentioning

confidence: 99%

EfficientNet Ensemble Learning: Identifying Ethiopian Medicinal Plant Species and Traditional Uses by Integrating Modern Technology with Ethnobotanical Wisdom

Kiflie,

Sharma,

Haile

et al. 2024

Computers

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Ethiopia is renowned for its rich biodiversity, supporting a diverse variety of medicinal plants with significant potential for therapeutic applications. In regions where modern healthcare facilities are scarce, traditional medicine emerges as a cost-effective and culturally aligned primary healthcare solution in developing countries. In Ethiopia, the majority of the population, around 80%, and for a significant proportion of their livestock, approximately 90% continue to prefer traditional medicine as their primary healthcare option. Nevertheless, the precise identification of specific plant parts and their associated uses has posed a formidable challenge due to the intricate nature of traditional healing practices. To address this challenge, we employed a majority based ensemble deep learning approach to identify medicinal plant parts and uses of Ethiopian indigenous medicinal plant species. The primary objective of this research is to achieve the precise identification of the parts and uses of Ethiopian medicinal plant species. To design our proposed model, EfficientNetB0, EfficientNetB2, and EfficientNetB4 were used as benchmark models and applied as a majority vote-based ensemble technique. This research underscores the potential of ensemble deep learning and transfer learning methodologies to accurately identify the parts and uses of Ethiopian indigenous medicinal plant species. Notably, our proposed EfficientNet-based ensemble deep learning approach demonstrated remarkable accuracy, achieving a significant test and validation accuracy of 99.96%. Future endeavors will prioritize expanding the dataset, refining feature-extraction techniques, and creating user-friendly interfaces to overcome current dataset limitations.

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“…Kato et al utilized contrastive learning for COVID-19 pneumonia classification from CT images, highlighting the efficacy of contrastive learning methods in training new classifiers following initial steps [19]. Wu and Lin investigated the impact of transfer learning on the performance of VGGNet-16 and ResNet-50 for classifying organic and residual waste [20]. Their study highlighted the benefits of deep learning with transfer learning in waste classification tasks, showcasing its potential in environmental applications.…”

Section: Motivationmentioning

confidence: 99%

Renal Pathological Image Classification Based on Contrastive and Transfer Learning

Liu,

Zhu,

Tian

et al. 2024

Electronics

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Following recent advancements in medical laboratory technology, the analysis of high-resolution renal pathological images has become increasingly important in the diagnosis and prognosis prediction of chronic nephritis. In particular, deep learning has been widely applied to computer-aided diagnosis, with an increasing number of models being used for the analysis of renal pathological images. The diversity of renal pathological images and the imbalance between data acquisition and annotation have placed a significant burden on pathologists trying to perform reliable and timely analysis. Transfer learning based on contrastive pretraining is emerging as a viable solution to this dilemma. By incorporating unlabeled positive pretraining images and a small number of labeled target images, a transfer learning model is proposed for high-accuracy renal pathological image classification tasks. The pretraining dataset used in this study includes 5000 mouse kidney pathological images from the Open TG-GATEs pathological image dataset (produced by the Toxicogenomics Informatics Project of the National Institutes of Biomedical Innovation, Health, and Nutrition in Japan). The transfer training dataset comprises 313 human immunoglobulin A (IgA) chronic nephritis images collected at Fukushima Medical University Hospital. The self-supervised contrastive learning algorithm “Bootstrap Your Own Latent” was adopted for pretraining a residual-network (ResNet)-50 backbone network to extract glomerulus feature expressions from the mouse kidney pathological images. The self-supervised pretrained weights were then used for transfer training on the labeled images of human IgA chronic nephritis pathology, culminating in a binary classification model for supervised learning. In four cross-validation experiments, the proposed model achieved an average classification accuracy of 92.2%, surpassing the 86.8% accuracy of the original RenNet-50 model. In conclusion, this approach successfully applied transfer learning through mouse renal pathological images to achieve high classification performance with human IgA renal pathological images.

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Effect of transfer learning on the performance of VGGNet-16 and ResNet-50 for the classification of organic and residual waste

Cited by 7 publications

References 34 publications

Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

EfficientNet Ensemble Learning: Identifying Ethiopian Medicinal Plant Species and Traditional Uses by Integrating Modern Technology with Ethnobotanical Wisdom

Renal Pathological Image Classification Based on Contrastive and Transfer Learning

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