Brief

Researchers have developed FAIR-Pruner, a new framework designed for automatic, layer-wise structured pruning of deep neural networks. This method adaptively allocates sparsity across network layers by using both removal-oriented and protection-oriented signals. Experiments across various datasets and model architectures, including vision models and a Qwen1.5-MoE model, demonstrate that FAIR-Pruner achieves strong accuracy-compression trade-offs. The framework is available as an open-source package. AI

Researchers have conducted a comprehensive comparison of various deep learning architectures for classifying COVID-19 from CT and X-ray lung imagery. The study utilized pre-trained models including VGG, Densenet, Resnet, MobileNet, Xception, EfficientNet, and NasNet. Results indicated that Resnet and VGG architectures achieved high accuracy, between 95% and 98%, in differentiating COVID-19 positive cases from healthy lungs, outperforming previous literature findings. AI