Knowledge is often highly specific to the conditions of acquisition, so there is limited transfer of learning from training to testing. A series of studies is reported examining specificity and transfer of lear
Loss of Specificity: While transfer learning can be great for generalization, it might lose some task-specific details. For example, if a model is trained to identify animals in general, it might not perform well at recognizing rare species because it hasn’t been exposed to them. Complexity:...
Pham et al[165]. focused their research on using 16 pre-trainedCNNs. In terms of accuracy, sensitivity, specificity, F1-score, and area under the curve, DenseNet-201 performed admirably. Their research showed that using transfer learning directly on the input slice yields better results than ...
As such, there is a need to pursue machine-learning strategies that can alleviate the dependence on high-fidelity data. In more general applications such as image classification or language processing, transfer-learning [40] has been established as an essential technique to address the scarcity of ...
Lastly, the RTGO algorithm was used to classify nutrient deficiency, and achieved 97.16% and 98.28% accuracy and specificity, respectively. Xu et al (2020) explored the performance of TL for nutrient deficiency diagnosis in rice by fine-tuning four models namely NasNetLarge, ResNet50, Inception...
Language transfer in language learning Susan M. GassLarry Selinker Jan 1985 1. List of Contributors 2. Preface 3. Introduction (by Gass, Susan M.) 4. A Role for the Mother Tongue (by Corder, S. Pit) 5. A New Account of Language Transfer (by Schachter, Jacquelyn) 6. Verification of ...
language processing techniques, we developed a customized transformer-and-attention-based OPED model (Fig.1) for the efficiency prediction and design optimization of pegRNAs. To improve its accuracy and generalizability, we introduced transfer learning to pre-train and fine-tune OPED. By working ...
Contrastive learning in protein language space predicts interactions between drugs and protein targets. Proc. Natl Acad. Sci. USA 120, e2220778120 (2023). Google Scholar Minervina, A. A. et al. SARS-CoV-2 antigen exposure history shapes phenotypes and specificity of memory CD8+ T cells. ...
function and trained parameters, i.e., weights and biases. As such, the intermediate products of these hidden layers, which act as latent neural representations or “features”, can be categorized by their specificity towards the low-fidelity prediction of\(C_{p,{PTFL}}\)depending on the ...
proposed a transfer learning strategy where they adopted ResNet-50 pre-trained weights on ImageNet and differentiated COVID-19 from other viral pneumonia. Pham et al [165]. focused their research on using 16 pre-trained CNNs. In terms of accuracy, sensitivity, specificity, F1-score, and ...