卷积神经网络分析.ppt

Presentation layout Introduction Drawbacks of previous neural networks Convolutional neural networks LeNet 5 Comparison Disadvantage Application Introduction Behavior of multilayer neural networks Multi-layer perceptron and image processing One or more hidden layers Sigmoid activations functions Drawbacks of previous neural networks the number of trainable parameters becomes extremely large Drawbacks of previous neural networks Little or no invariance to shifting, scaling, and other forms of distortion Drawbacks of previous neural networks Little or no invariance to shifting, scaling, and other forms of distortion Drawbacks of previous neural networks Drawbacks of previous neural networks scaling, and other forms of distortion Drawbacks of previous neural networks the topology of the input data is completely ignored work with raw data. Drawbacks of previous neural networks Black and white patterns: Gray scale patterns : Drawbacks of previous neural networks Improvement Fully connected network of sufficient size can produce outputs that are invariant with respect to such variations. Training time Network size Free parameters History In 1995, Yann LeCun and Yoshua Bengio introduced the concept of convolutional neural networks. About CNN’s CNN’s Were neurobiologically motivated by the findings of locally sensitive and orientation-selective nerve cells in the visual cortex. They designed a network structure that implicitly extracts relevant features. Convolutional Neural Networks are a special kind of multi-layer neural networks. About CNN’s CNN is a feed-forward network that can extract topological properties from an image. Like almost every other neural networks they are trained with a version of the back-propagation algorithm. Convolutional Neural Networks are designed to recognize visual patterns directly from pixel images with minimal preprocessing. They can recognize patterns with extreme variability (such as handwritten char