Remove 2011 Remove Convolutional Neural Networks Remove Machine Learning
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Revolutionizing Image Classification: Training Large Convolutional Neural Networks on the ImageNet Dataset

Marktechpost

The success of this model reflects a broader shift in computer vision towards machine learning approaches that leverage large datasets and computational power. Previously, researchers doubted that neural networks could solve complex visual tasks without hand-designed systems. by the next-best model.

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Meet the Research Scientist: Shirley Ho

NYU Center for Data Science

Meet CDS Senior Research Scientist Shirley Ho , a distinguished astrophysicist and machine learning expert who brings a wealth of experience and innovative research to our community. She led the first effort to accelerate astrophysical simulations with deep learning. Ho’s contributions have not gone unnoticed.

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The Evolution of ImageNet and Its Applications

Viso.ai

It brings the development of deep learning models for image classification , object detection , and other computer vision tasks. 2011 – A good ILSVRC image classification error rate is 25%. 2012 – A deep convolutional neural net called AlexNet achieves a 16% error rate. parameters and achieved 84.5%

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Using JPEG Compression to Improve Neural Network Training

Unite.AI

A new research paper from Canada has proposed a framework that deliberately introduces JPEG compression into the training scheme of a neural network, and manages to obtain better results – and better resistance to adversarial attacks. Architectures used were EfficientFormer-L1 ; ResNet ; VGG ; MobileNet ; and ShuffleNet.

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Top Computer Vision Papers of All Time (Updated 2024)

Viso.ai

Today’s boom in computer vision (CV) started at the beginning of the 21 st century with the breakthrough of deep learning models and convolutional neural networks (CNN). They found that removing any convolutional layer (each containing less than 1% of the model’s parameters) resulted in inferior performance.

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Pascal VOC Dataset: A Technical Deep Dive (2024 Guide)

Viso.ai

VOC2011 PASCAL VOC challenge took a big step forward in 2011 with VOC2011. Deep Learning Approaches Convolutional Neural Networks (CNNs) : The CNNs including AlexNet , VGGNet , and ResNet helped solve computer vision problems by learning the hierarchal features directly from the Pascal VOC data. What’s Next?

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Dude, Where’s My Neural Net? An Informal and Slightly Personal History

Lexalytics

Initially, we had been using classic symbolic NLP algorithms, but in recent years we had started to incorporate machine learning (ML) models into more and more parts of our code, including our own implementations of conditional random fields [ 11 ] and a home-grown maximum entropy classifier. So, whatever did happen to neural networks?