ResNet50

Team Hope's ResNet34 model, fine-tuned with ImageNet pretrained weights for 25 epochs using the Adam optimizer and a learning rate of 1e-4, achieved 81% accuracy on the NakbaArchiveClassifier Shared T…

Researchers propose a decoupled transfer learning strategy that adapts normalization layers and precomputes features once, reducing training overhead without end-to-end backpropagation. Evaluated on f…

Researchers developed a hybrid machine learning approach combining EfficientNetB0 feature extraction with Random Forest classification to achieve 94.58% accuracy and sub-millisecond inference for real…

Researchers developed HRVConformer, a deep learning architecture that classifies neonatal hypoxic-ischemic encephalopathy (HIE) by directly processing raw heart rate signals, achieving an AUC of 83.23…