Going Deeper With Convolutions Bibtex

Going deeper with convolutions bibtex. This was achieved by a. And these methods have given more successful results as compared to traditional methods. We propose a deep convolutional neural network architecture codenamed inception which was responsible for setting the new state of the art for classification and detection in the imagenet large scale visual recognition challenge 2014 ilsvrc 2014. In this paper we propose a novel algorithmic technique for generating an snn with a deep architecture.

In this post i would like to discuss about one specific task in computer vision called as semantic segmentation even though researchers have come up with numerous ways to solve this problem i will talk about a particular architecture namely unet which use a. Pierre sermanet google inc. Wei liu university of north carolina chapel hill yangqing jia google inc. In our case the word deep is used in two different meanings.

Deep learning has enabled the field of computer vision to advance rapidly in the last few years. Over the past few years spiking neural networks snns have become popular as a possible pathway to enable low power event driven neuromorphic hardware. However their application in machine learning have largely been limited to very shallow neural network architectures for simple problems. Going deeper with convolutions christian szegedy google inc.

The major difference between deep learning and classical recognition methods is that deep learning methods consider an end to. In conjunction with the famous we need to go deeper internet meme 1. A not for profit organization ieee is the world s largest technical professional organization dedicated to advancing technology for the benefit of humanity. In this paper we go deeper with the embedded fpga platform on accelerating cnns and propose a cnn accelerator design on embedded fpga for image net large scale image classification.

Dumitru erhan google inc. Going deeper with convolutions szegedy christian and liu wei and jia yangqing and sermanet pierre and reed scott and anguelov dragomir and erhan dumitru and vanhoucke vincent and rabinovich andrew arxiv e print archive 2014 via local bibsonomy keywords. Going deeper with convolutions. The main hallmark of this architecture is the improved utilization of the computing resources inside the network.

Cv is a very interdisciplinary field. Scott reed university of michigan dragomir anguelov google inc. In recent years deep learning methods have come to the forefront in many areas that require remote sensing from medicine to agriculture from defense industry to space research.