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

Marktechpost

Previously, researchers doubted that neural networks could solve complex visual tasks without hand-designed systems. However, this work demonstrated that with sufficient data and computational resources, deep learning models can learn complex features through a general-purpose algorithm like backpropagation.

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Meet the Research Scientist: Shirley Ho

NYU Center for Data Science

What sets Dr. Ho apart is her pioneering work in applying deep learning techniques to astrophysics. 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.

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The Evolution of ImageNet and Its Applications

Viso.ai

Image classification employs AI-based deep learning models to analyze images and perform object recognition, as well as a human operator. It is one of the largest resources available for training deep learning models in object recognition tasks. 2011 – A good ILSVRC image classification error rate is 25%.

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Using JPEG Compression to Improve Neural Network Training

Unite.AI

JPEG-DL Instead, the new work , titled JPEG Inspired Deep Learning , offers a much simpler architecture, which can even be imposed upon existing models. Data and Tests JPEG-DL was evaluated against transformer-based architectures and convolutional neural networks (CNNs).

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

Viso.ai

Our software helps several leading organizations start with computer vision and implement deep learning models efficiently with minimal overhead for various downstream tasks. VOC2011 PASCAL VOC challenge took a big step forward in 2011 with VOC2011. provides a robust end-to-end computer vision infrastructure – Viso Suite.

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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). We split them into two categories – classical CV approaches, and papers based on deep-learning. Find the SURF paper here.

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

Lexalytics

They were not wrong: the results they found about the limitations of perceptrons still apply even to the more sophisticated deep-learning networks of today. This book effectively killed off interest in neural networks at that time, and Rosenblatt, who died shortly thereafter in a boating accident, was unable to defend his ideas.