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Revolutionizing Image Classification: Training Large Convolutional Neural Networks on the ImageNet Dataset

Marktechpost

Further, pre-training on the ImageNet Fall 2011 dataset, followed by fine-tuning, reduced the error to 15.3%. In the ILSVRC-2012 competition, the model reached a top-5 validation error rate of 18.2%, which improved to 16.4% when predictions from five CNNs were averaged.

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Meet the Research Scientist: Shirley Ho

NYU Center for Data Science

Dr. Ho’s innovative approach has led to several groundbreaking achievements: Her team at Carnegie Mellon University was the first to apply 3D convolutional neural networks in astrophysics. “I look forward to collaborating with fellow researchers and students to explore new frontiers in foundation models for science.”

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The Evolution of ImageNet and Its Applications

Viso.ai

2011 – A good ILSVRC image classification error rate is 25%. 2012 – A deep convolutional neural net called AlexNet achieves a 16% error rate. 2015 – Microsoft researchers report that their Convolutional Neural Networks (CNNs) exceed human ability in pure ILSVRC tasks.

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Using JPEG Compression to Improve Neural Network Training

Unite.AI

Data and Tests JPEG-DL was evaluated against transformer-based architectures and convolutional neural networks (CNNs). Architectures used were EfficientFormer-L1 ; ResNet ; VGG ; MobileNet ; and ShuffleNet. The ResNet versions used were specific to the CIFAR dataset: ResNet32, ResNet56, and ResNet110.

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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.

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

Lexalytics

A paper that exemplifies the Classifier Cage Match era is LeCun et al [ 109 ], which pits support vector machines (SVMs), k-nearest neighbor (KNN) classifiers, and convolution neural networks (CNNs) against each other to recognize images from the NORB database. 90,575 trainable parameters, placing it in the small-feature regime.