Convolution neural network
Convolutional neural network?
A Convolutional Neural Network (CNN) is a type of deep learning model specifically designed for analyzing visual data such as images and videos. It is inspired by how the human brain processes visual information.
CNNs consist of multiple layers, including convolutional layers, pooling layers, and fully connected layers. The convolutional layers perform feature extraction by applying a set of learnable filters to the input data to detect various patterns and features. The pooling layers then downsample the extracted features, reducing the spatial dimensions and complexity of the data. Finally, the fully connected layers perform classification or regression tasks based on the features learned by the previous layers.
Why do we use CNN instead of ANN?
1. Parameter Sharing: CNNs leverage parameter sharing, which significantly reduces the number of parameters that need to be learned. In fully connected Artificial Neural Networks (ANNs), every neuron is connected to every neuron in the preceding and succeeding layers, resulting in many parameters. However, CNNs use shared weights in the convolutional layers, which allows them to learn features that are invariant to translation. This parameter-sharing property makes CNNs more efficient for handling large visual datasets and enables them to generalize well.
2. Spatial Hierarchies: CNNs can capture spatial hierarchies of features within an image. Convolutional layers in CNNs apply filters to input data, performing operations such as edge detection, texture recognition, and corner detection. As the network progresses through multiple layers, these filters learn more complex and abstract features, gradually building a hierarchical representation of the image. This spatial hierarchy is crucial for understanding the intricate visual patterns present in images and is difficult to achieve with fully connected ANNs.
3. Translation Invariance: CNNs are invariant to translation, meaning they can recognize and identify objects regardless of their position within an image. This property is achieved through the convolutional and pooling operations. Convolutional layers scan the entire image, irrespective of location, to detect relevant features. Pooling layers downsample the data and select the most prominent features, making the network robust to minor translations or distortions in the input data.
Considering these advantages, CNNs are particularly effective for visual recognition tasks like image classification, object detection, and image segmentation. They exploit the spatial structure and properties of images, making them more efficient and accurate compared to fully connected ANNs for such tasks.