Add to ORKGWe propose a deep convolutional neural network architecture codenamed Incep-tion, which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. This was achieved by a carefully crafted design that allows for increasing the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers dee...
The purpose of the work, the results of which are presented within the framework of the article, was...
Deep convolutional neural networks have been successfully applied to many image-processing problems ...
Object of research: basic architectures of deep learning neural networks. Investigated problem: ins...
In this work we investigate the effect of the convolutional network depth on its accuracy in the lar...
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution im...
Deep artificial neural networks are showing a lot of promise when it comes to tasks involving images...
Deep (machine) learning in recent years has significantly increased the predictive modeling strength...
In the last two decades, deep learning, an area of machine learning has made exponential progress an...
Convolutional networks are at the core of most stateof-the-art computer vision solutions for a wide...
Recent years have witnessed two seemingly opposite developments of deep convolutional neural network...
The successful application of ConvNets and other neural architectures to computer vision is central ...
Deep convolutional neural networks have been successfully applied to many image-processing problems ...
In very recent years, several classification problems in computer vision, have boosted its performan...
Recent advances in Convolutional Neural Networks (CNNs) have obtained promising results in difficult...
Computational visual perception, also known as computer vision, is a field of artificial intelligenc...
The purpose of the work, the results of which are presented within the framework of the article, was...
Deep convolutional neural networks have been successfully applied to many image-processing problems ...
Object of research: basic architectures of deep learning neural networks. Investigated problem: ins...
In this work we investigate the effect of the convolutional network depth on its accuracy in the lar...
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution im...
Deep artificial neural networks are showing a lot of promise when it comes to tasks involving images...
Deep (machine) learning in recent years has significantly increased the predictive modeling strength...
In the last two decades, deep learning, an area of machine learning has made exponential progress an...
Convolutional networks are at the core of most stateof-the-art computer vision solutions for a wide...
Recent years have witnessed two seemingly opposite developments of deep convolutional neural network...
The successful application of ConvNets and other neural architectures to computer vision is central ...
Deep convolutional neural networks have been successfully applied to many image-processing problems ...
In very recent years, several classification problems in computer vision, have boosted its performan...
Recent advances in Convolutional Neural Networks (CNNs) have obtained promising results in difficult...
Computational visual perception, also known as computer vision, is a field of artificial intelligenc...
The purpose of the work, the results of which are presented within the framework of the article, was...
Deep convolutional neural networks have been successfully applied to many image-processing problems ...
Object of research: basic architectures of deep learning neural networks. Investigated problem: ins...