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ENTITY DenseNet 121

DenseNet 121

PulseAugur coverage of DenseNet 121 — every cluster mentioning DenseNet 121 across labs, papers, and developer communities, ranked by signal.

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Total · 30d
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Releases · 30d
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Papers · 30d
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TIER MIX · 90D
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SENTIMENT · 30D

3 day(s) with sentiment data

RECENT · PAGE 1/1 · 6 TOTAL
  1. TOOL · CL_105119 ·

    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…

  2. TOOL · CL_84859 ·

    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…

  3. TOOL · CL_82763 ·

    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…

  4. TOOL · CL_44889 ·

    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…

  5. RESEARCH · CL_44710 ·

    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…

  6. RESEARCH · CL_21777 ·

    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…