Difference between revisions of "Convolutional Neural Network (CNN)"
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| − | A '''Convolutional Neural Network ( | + | A '''Convolutional Neural Network (CNN)''' is a class of deep neural networks most commonly applied to visual tasks, such as image and video recognition. CNNs are also known for their exceptional performance in tasks related to spatial hierarchies, making them applicable to other forms of data with similar properties. |
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| + | |||
| + | == History == | ||
| + | The development of CNNs can be traced back to the Neocognitron introduced by Kunihiko Fukushima in 1980. However, CNNs gained popularity after AlexNet, a particular kind of CNN architecture, won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012. | ||
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| + | |||
| + | == Architecture == | ||
| + | *Convolutional Layer: The convolutional layer is the core building block of a CNN. It filters inputs for useful information. These convolutional layers have parameters that are learned to identify various types of features like edges, corners, and textures. | ||
| + | *Pooling Layer: Pooling (or subsampling) reduces the dimensionality of each feature map and retains the most essential information, thus making the detection of features invariant to scale and orientation changes. | ||
| + | *Fully Connected Layer: Fully connected layers perform classification on the features formed by the convolutional layers and down-sampled by the pooling layers. In a CNN, the fully connected layer is generally followed by an output layer, activation function, or classifier. | ||
| + | *Activation Functions: Activation functions introduce nonlinear properties into the network. The commonly used activation functions in CNNs are ReLU (Rectified Linear Unit), Sigmoid, and Tanh. | ||
| + | |||
| + | |||
| + | == Applications == | ||
| + | *Image Recognition: CNNs are extensively used in image classification tasks, for everything from identifying objects in photos to diagnosing diseases from medical imaging. | ||
| + | *Natural Language Processing: Although less common, CNNs have been successfully applied in Natural Language Processing (NLP), often combined with other types of neural networks like RNNs. | ||
| + | *Video Analysis: CNNs are also employed in video analysis for tasks such as object detection and recognition over time, activity recognition, and video classification. | ||
| + | |||
| + | |||
| + | == Training == | ||
| + | *Backpropagation: CNNs, like all neural networks, use backpropagation for training. It involves computing the gradient of the loss function concerning each weight by the chain rule. | ||
| + | *Optimization Algorithms: Common optimization algorithms used for training CNNs include Stochastic Gradient Descent (SGD), Adam, and RMSprop. | ||
| + | *Software Libraries: Popular software libraries for implementing CNNs include TensorFlow, Keras, and PyTorch. | ||
| + | |||
| + | |||
| + | == Challenges and Limitations == | ||
| + | *Requires a large amount of labeled data for training | ||
| + | *Computationally expensive | ||
| + | *Vulnerable to adversarial attacks | ||
| + | |||
| + | |||
| + | == Future Trends == | ||
| + | *Real-time processing | ||
| + | *Energy-efficient CNN architectures | ||
| + | *Integration with other neural network architectures for various applications | ||
| + | |||
| + | |||
| + | == See Also == | ||
| + | *[[Deep Learning]] | ||
| + | *[[Artificial Neural Network (ANN)]] | ||
| + | *[[Natural Language Processing (NLP)]] | ||
| + | *[[Neural Network]] | ||
| + | __NOTOC__ | ||
Latest revision as of 17:58, 4 September 2023
A Convolutional Neural Network (CNN) is a class of deep neural networks most commonly applied to visual tasks, such as image and video recognition. CNNs are also known for their exceptional performance in tasks related to spatial hierarchies, making them applicable to other forms of data with similar properties.
History
The development of CNNs can be traced back to the Neocognitron introduced by Kunihiko Fukushima in 1980. However, CNNs gained popularity after AlexNet, a particular kind of CNN architecture, won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012.
Architecture
- Convolutional Layer: The convolutional layer is the core building block of a CNN. It filters inputs for useful information. These convolutional layers have parameters that are learned to identify various types of features like edges, corners, and textures.
- Pooling Layer: Pooling (or subsampling) reduces the dimensionality of each feature map and retains the most essential information, thus making the detection of features invariant to scale and orientation changes.
- Fully Connected Layer: Fully connected layers perform classification on the features formed by the convolutional layers and down-sampled by the pooling layers. In a CNN, the fully connected layer is generally followed by an output layer, activation function, or classifier.
- Activation Functions: Activation functions introduce nonlinear properties into the network. The commonly used activation functions in CNNs are ReLU (Rectified Linear Unit), Sigmoid, and Tanh.
Applications
- Image Recognition: CNNs are extensively used in image classification tasks, for everything from identifying objects in photos to diagnosing diseases from medical imaging.
- Natural Language Processing: Although less common, CNNs have been successfully applied in Natural Language Processing (NLP), often combined with other types of neural networks like RNNs.
- Video Analysis: CNNs are also employed in video analysis for tasks such as object detection and recognition over time, activity recognition, and video classification.
Training
- Backpropagation: CNNs, like all neural networks, use backpropagation for training. It involves computing the gradient of the loss function concerning each weight by the chain rule.
- Optimization Algorithms: Common optimization algorithms used for training CNNs include Stochastic Gradient Descent (SGD), Adam, and RMSprop.
- Software Libraries: Popular software libraries for implementing CNNs include TensorFlow, Keras, and PyTorch.
Challenges and Limitations
- Requires a large amount of labeled data for training
- Computationally expensive
- Vulnerable to adversarial attacks
Future Trends
- Real-time processing
- Energy-efficient CNN architectures
- Integration with other neural network architectures for various applications