'Convolutional networks' presentation slideshows

OTHER NETWORKS ARCHITECTURE

OTHER NETWORKS ARCHITECTURE. Yuguang Lin, Aashi Jain , Siddarth Ravichandran Presenters CSE 291 G00 Deep Learning for Sequences. TRELLIS NETWORK FOR SEQUENCE MODELLING. Yuguang Lin, presenter. Sequence Model. Background. Three popular ways for sequence modelling

783 views • 78 slides

A brief introduction to graph convolution

A brief introduction to graph convolution. Zhizhong LI Dec 20, 2018. Contents. Introduction Spectral Graph Convolution Graph Convolutions. Introduction. Notations Graph data and tasks Processing tools. Notation. Adjacency matrix. Data on graph. Citation. Per-graph prediction.

1.09k views • 62 slides

Chapter 9 Convolutional Networks

Chapter 9 Convolutional Networks. Ruchengsen 2016.11.01. Convolutional networks are simply neural networks that use convolution in place of general matrix multiplication in at least one of their layers. Outline. The Convolution Operation Motivation Pooling

742 views • 54 slides

Introduction: Convolutional Neural Networks for Visual Recognition

Introduction: Convolutional Neural Networks for Visual Recognition. b oris .ginzburg@intel.com. Acknowledgments. This presentation is heavily based on: http ://cs.nyu.edu/~ fergus/pmwiki/pmwiki.php http://deeplearning.net/reading-list/tutorials /

2.37k views • 45 slides

Image Classification

Image Classification. Convolutional networks - Why. Convolutions Reduce parameters Capture shift-invariance: location of patch in image should not matter Subsampling Allows greater invariance to deformations Allows the capture of large patterns with small filters.

897 views • 43 slides

Semantic Segmentation

Semantic Segmentation. The Task. person. grass. trees. motorbike. road. Evaluation metric. Pixel classification! Accuracy? Heavily unbalanced Common classes are over-emphasized Intersection over Union Average across classes and images Per-class accuracy

1.51k views • 24 slides

What is the Best Multi-Stage Architecture for Object Recognition

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Heterogeneous convolutional neural networks for visual recognition

Heterogeneous convolutional neural networks for visual recognition. Xiangyang Li, Luis Herranz , Shuqiang Jiang Key Laboratory of Intelligent Information Processing Institute of Computing Technology , CAS. Heterogeneous convolutional neural networks for visual recognition.

218 views • 20 slides

Convolutional neural network for Image recognition

Convolutional neural network for Image recognition. Reference http://cs231n.github.io/convolutional-networks/. By Yunzhe Xue. Dense neural network and Convolutional neural network. A simple CNN structure. CONV: Convolutional kernel layer RELU: Activation function

225 views • 17 slides

MACHINE LEARNING AND ARTIFICIAL NEURAL NETWORKS FOR FACE VERIFICATION

MACHINE LEARNING AND ARTIFICIAL NEURAL NETWORKS FOR FACE VERIFICATION. Alexis Zubiolo Morpheme Team. Why do we need AI in Computer Vision?. The brain is really good at some pattern recognition tasks ( e.g . face detection / verification )

322 views • 13 slides