SignSGD with pre-sign dithering and a calibrated hybrid switch to SGD achieves 92.18% accuracy on CIFAR-10 with ResNet-18, outperforming pure SGD and SignSGD, plus better results than Adam on CIFAR-100.
Deep residual learning for image recognition,
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A Float16-quantized MobileNetV2 model for multi-class brain tumor MRI classification reaches 82.37% validation accuracy while shrinking from 35.34 MB to 5.76 MB, a 6.14x reduction with negligible accuracy change.
Ensemble of three standard CNNs trained on PlantVillage images reaches 99.23% accuracy on 15-class plant disease classification.
citing papers explorer
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Enhancing SignSGD: Small-Batch Convergence Analysis and a Hybrid Switching Strategy
SignSGD with pre-sign dithering and a calibrated hybrid switch to SGD achieves 92.18% accuracy on CIFAR-10 with ResNet-18, outperforming pure SGD and SignSGD, plus better results than Adam on CIFAR-100.
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Quantized Machine Learning Models for Medical Imaging in Low-Resource Healthcare Settings
A Float16-quantized MobileNetV2 model for multi-class brain tumor MRI classification reaches 82.37% validation accuracy while shrinking from 35.34 MB to 5.76 MB, a 6.14x reduction with negligible accuracy change.
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AgriMind: An Ensemble Deep Learning Framework for Multi-Class Plant Disease Classification
Ensemble of three standard CNNs trained on PlantVillage images reaches 99.23% accuracy on 15-class plant disease classification.