Inceptionv3
PulseAugur coverage of Inceptionv3 — every cluster mentioning Inceptionv3 across labs, papers, and developer communities, ranked by signal.
4 day(s) with sentiment data
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AI framework improves appendicitis grading from ultrasound images
Researchers have developed AppendiGrade, a deep learning framework designed to improve the grading of appendicitis from ultrasound images. The system utilizes four pre-trained models, including InceptionV3, which achiev…
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New research questions Fréchet Inception Distance trustworthiness
A new paper published on arXiv explores the trustworthiness of the Fréchet Inception Distance (FID) metric, commonly used to evaluate synthetic image quality. The research, authored by Ciaran Bench, investigates how sto…
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Deep learning predicts network hardware failure using thermal imaging and sensor fusion
Researchers have developed a deep learning strategy for predictive maintenance of network hardware, utilizing thermal imaging and power sensor data. The study evaluated several models, including ResNet-50, InceptionV3, …
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Lightweight CNNs outperform larger models in satellite land-cover segmentation
A new study benchmarks five convolutional neural network (CNN) architectures for satellite land-cover segmentation, focusing on the efficiency-accuracy trade-off. The research found that MobileNetV2_v1, a lightweight mo…
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AI explanation stability claims scientifically invalid without cross-method validation, study finds
A new position paper argues that claims about the stability of AI model explanations are scientifically invalid unless validated across multiple methods. Experiments with DenseNet201, ResNet50V2, and InceptionV3 showed …
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InferNet exploits GPU profiles for DNN architecture inference
Researchers have developed InferNet, a novel method for inferring the architecture of deep neural networks (DNNs) by analyzing aggregate GPU profiles. This technique bypasses the need for complex, fine-grained data anal…
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New Binary Iterative Method Enhances Adversarial Attack Generation
Researchers have introduced a new method called the "Binary Iterative Method" (BinIM) for generating non-targeted adversarial attacks on deep learning models. This method employs a divide-and-conquer strategy to optimiz…
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Deep Learning Models Achieve High Accuracy in Plant Disease Classification
Researchers have developed advanced deep learning frameworks for classifying plant diseases from leaf images, achieving high accuracy rates. One study focused on lemon leaf disease, utilizing ensemble models like Incept…
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AI model accurately classifies peach leaf damage with attention mechanisms
Researchers have developed a new deep learning model for classifying peach leaf damage, achieving high accuracy on a benchmark dataset. The model, an enhanced EfficientNetB5 incorporating a Convolutional Block Attention…
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Synthetic MRIs offer modest gains in tumor classification for specific AI models
Researchers investigated the effectiveness of synthetic brain MRI images generated by StyleGAN2-ADA for improving tumor classification tasks. They found that while a GPT-5.5 model could only slightly distinguish synthet…
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CNNs achieve 96% accuracy classifying partial discharge using novel AWA patterns
Researchers have developed a novel Amplitude-Width-Area (AWA) pattern representation to analyze partial discharge (PD) pulses under switching-voltage excitation. This method maps PD pulses into visual patterns using amp…