DenseNet 121
PulseAugur coverage of DenseNet 121 — every cluster mentioning DenseNet 121 across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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OliveGemma model excels at Mediterranean diet recognition
Researchers have developed OliveGemma, a new 3 billion parameter vision-language model specifically designed for recognizing Mediterranean and European cuisine. Built upon the PaliGemma-2-3B architecture and fine-tuned …
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AI research tackles chest radiograph classification with multimodal data
A new research paper published on arXiv explores methods for classifying chest radiographs using multimodal data, including clinical indications and image findings. The study evaluated various fusion techniques, such as…
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AI system Inspectra CXR v5 achieves high accuracy in Thai thoracic disease detection · 2 sources tracked
Researchers have developed Inspectra CXR version 5, a deep learning system designed to detect thoracic diseases from chest radiographs in Thailand. The system, which combines classification and lesion localization, was …
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New MoE framework integrates diverse architectures for improved plant disease classification
Researchers have developed a novel adaptive soft Mixture-of-Experts (MoE) framework designed to improve plant leaf disease classification. This framework integrates three distinct architectures—EfficientNet-B0, DenseNet…
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Deep learning models show promise in detecting facial spoofing attacks
This research paper investigates the use of deep learning models, specifically MobileNetV2, DenseNet-121, and Inception-v3, for detecting spoofing attacks in facial recognition systems. Using the CelebA-Spoof dataset, t…
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New MRI benchmark and federated learning framework for pancreatic cancer risk
Researchers have introduced Cyst-X, a new benchmark dataset and federated learning framework designed to improve the early detection of pancreatic cystic neoplasms. This initiative aims to address the challenges in stra…
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Research explores how sparsity allocation affects neural network recovery after pruning
A new research paper investigates how the allocation of sparsity in neural networks impacts their ability to recover accuracy after pruning, especially when labeled retraining data is unavailable. The study compares dif…
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Deep Learning Models Achieve High Accuracy in COVID-19 CT Lesion Prediction
Researchers have evaluated deep learning architectures for predicting COVID-19 lesions in CT scans, addressing the lack of standardized performance analysis in medical image segmentation. The study integrated four segme…
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GRALIS framework unifies linear attribution methods for deep neural networks
Researchers have introduced GRALIS, a novel mathematical framework designed to unify various linear attribution methods used in Explainable AI (XAI). This framework establishes a canonical representation for attribution…