What is a convolutional neural network?
Convolutional Neural Networks (CNNs or ConvNETs) are Deep Learning algorithms that process images, assign importance to objects in the image using learnable weights and biases, and can differentiate images from each other.
A convolutional neural network is essentially a neural network that uses a convolution layer and pooling layer. The convolutional layer convolves into a smaller area to extract features, while the pooling layer picks the data with the highest value within an area.
They require less pre-processing in comparison to other classification algorithms and are able to learn filters and characteristics.
The architecture of convolutional neural networks was based on the organization of the visual cortex.
They use computer vision, natural language processing, and recommender systems to perform generative and descriptive tasks.
This is how convolution can be defined:
“Looking at a function’s surroundings to make better/accurate predictions of its outcome.”
- Dr. Prasad Samarakoon
How do convolutional neural networks work?
Convolutional neural networks have multiple layers.
The first layer is the Convolutional Layer. This is the base of the system and does most of the computational work. It uses filters to convolve data.
It takes the element-wise product of filters in the image and proceeds to add the values for every sliding action. This filter can also be referred to as a neuron or a kernel.
The next layer is the Activation Layer. This layer applies the Rectified Linear Unit to increase non-linearity in the ConvNET.
After that, the Pooling Layer focuses on the downsampling of features. There usually are hyperparameters in the pooling layer.
It reduces the spatial size of the representation, leading to a reduction in the required amount of computation and weights by replacing the output of the CNN at specific locations through the derivation of a summary statistic of the nearby outputs.
Pooling adds translation invariance, making the objects recognizable, irrespective of where they appear on the frame.
The following layer is the Fully Connected Layer. This layer focuses on flattening, i.e., turning the pooled feature map matrix into a single column that the neural network then processes.
This layer aids in mapping the representation between the input and the output.
What are convolutional neural networks used for?
Here are some of the common applications of convolutional neural networks:
- Semantic segmentation: Researchers have used convolutional neural networks to improve semantic segmentation models by incorporating rich information.
- Object detection: Convolutional neural networks are used in self-driving cars as well as facial recognition systems for object detection.
- Image captioning: Convolutional neural networks are used to caption and describe images, making it easier for visually impaired people to understand what the images are trying to convey. It is even used heavily by YouTube.
- Voice synthesis: Google Assistant’s voice synthesizer uses Deepmind’s WaveNet ConvNet model.
- Astrophysics: they are used to make sense of radio telescope data and predict the probable visual image to represent that data.
Convolutional neural networks have even found applications to some extent in population genetic inference as well as in disease identification. They are also used for the purpose of fraud detection.
What are the advantages of convolutional neural networks?
Here are the most significant advantages of convolutional neural networks (CNNs):
- CNNs do not require human supervision for the task of identifying important features.
- They are very accurate at image recognition and classification.
- Weight sharing is another major advantage of CNNs.
- Convolutional neural networks also minimize computation in comparison with a regular neural network.
- CNNs make use of the same knowledge across all image locations.
What are the disadvantages of convolutional neural networks?
Some of the disadvantages of CNNs: include the fact that a lot of training data is needed for the CNN to be effective and that they fail to encode the position and orientation of objects.
- They fail to encode the position and orientation of objects. They have a hard time classifying images with differnet positions.
- A lot of training data is needed for the CNN to be effective.
- CNNs tend to be much slower because of operations like maxpool.
- In case the convolutional neural network is made up of multiple layers, the training process could take a particularly long time if the computer does not have a good GPU.
- Convolutional neural networks will recognize the image as clusters of pixels which are arranged in distinct patterns. They don’t understand them as components present in the image.
What is the difference between a convolutional and deep neural network?
The biggest difference between convolutional neural networks and other deep neural networks is that because hierarchical patch-based convolution operations are employed in CNNs, the computational costs are reduced and images are abstracted on different feature levels.
A CNN could be useful for reducing the number of parameters we need to train without you have to sacrifice on performance. However, training a conversational neural network tends to be a bit slower than training a DNN.
A DNN (Deep neural network) could be a convolutional neural network or it could be a plain multilayer perceptron.
Is convolutional neural network better than recurrent neural network?
Convolutional neural networks are generally used to solve problems that are related to spatial data like images. RNNs are used more often to analyze temporal, sequential data like text or videos.
Their architectures are also different. CNNs are feed-forward neural networks which use filters and pooling layers, while RNNs feed the results back into the network.
CNNS are considered to be rather powerful in comparison with RNNs, and RNNs use less feature compatibility compared with CNNs.
Using a convolutional neural network is best for processing images and videos, while recurrent neural networks analyzing text and speech.
In CNNs, inputs should be of fixed sizes, and they’ll even generate outputs of fixed sizes. RNNs, on the other hand, can deal with inputs and outputs of arbitary lengths.