What is a Convolutional Neural Network (CNN)?
Specialised deep learning models like
Convolutional Neural Networks (CNNs) are made to process and analyse visual
data. CNNs, as opposed to traditional neural networks, are the preferred
architecture for computer vision tasks like object detection, image
segmentation, and image classification because of their exceptional ability to
detect patterns, textures, and spatial hierarchies in images.
How Does a CNN Work?
A CNN consists of several
layers that work together to extract features from the input data (usually
images). Here’s a brief overview of the main components:
- Convolutional Layer:
This is a CNN's fundamental component. To generate feature maps, it applies a
collection of filters—also referred to as kernels—across the input image. Every
filter is made to identify a particular feature, such as colours, textures, or
edges.
Convolution is a mathematical process that creates a feature map by moving the
filter across the input image and calculating the dot product between the
filter and certain areas of the image.
- Activation Function:
o Following convolution, the model is
subjected to element-wise application of an activation function (typically
ReLU, or Rectified Linear Unit) to add non-linearity and help it learn
intricate patterns.
- Pooling Layer:
o Pooling (often Max Pooling) is used to
downsample the feature maps, reducing their spatial dimensions while retaining
the most important features. This helps in reducing the computational load and
controlling overfitting.
- Fully Connected Layer:
o After several convolutional and pooling
layers, the feature maps are flattened into a vector and passed through fully
connected layers, similar to those in traditional neural networks. These layers
perform the final classification or regression tasks.
- Output Layer:
o The output layer produces the final
predictions, typically using a softmax function for classification tasks.
Applications of CNNs
1. Image Classification: o Classifying images into various categories is a common use for CNNs.
Facial recognition, medical image classification, and animal identification in
photographs are a few examples.
2. Object Detection: Using bounding boxes, CNNs are used to locate objects in pictures and
videos. Applications include security surveillance and self-driving cars that
can recognise traffic signs and pedestrians.
3. Image Segmentation: o By using CNNs to divide an image into segments according to pixel
properties, tasks like medical image analysis—which identifies regions of
interest, such as tumors—are made possible.
4. Facial Recognition: o Facial recognition systems for mobile phone authentication, social
media tagging, and security are powered by CNNs.
Advantages of CNNs
1. Automated Extraction of Features:
CNNs
automatically learn features from raw data, doing away with the need for human
feature engineering, in contrast to traditional models.
2. Spatial Invariance:
CNNs are
very good at identifying spatial hierarchies in pictures, so they can identify
objects in pictures no matter where they are in the picture.
3. Parameter Sharing:
Compared to fully connected networks,
convolutional layers significantly reduce the number of parameters and
computational cost by sharing filters throughout the entire image.
4. High Accuracy:
CNNs perform at the cutting edge in a variety
of tasks, especially those that involve images.
Disadvantages of CNNs
1. High computational cost: Training CNNs necessitates a substantial amount of computational
resources, such as strong GPUs, sizable datasets, and a significant amount of
time.
2. Large Dataset Requirement: Applications with limited data availability may find it difficult to
train CNNs on large, annotated datasets, which is where they perform best.
3. Lack of Interpretability: Because it can be challenging to comprehend the precise features a
neural network uses to make predictions, neural networks are frequently
referred to as "black boxes".
4. Overfitting: CNNs are
prone to overfitting when improper regularisation techniques are used,
particularly when working with small datasets or extremely complex models.
5. Sensitivity to Input Variations:
Unless specifically taken into account in the architecture, CNNs can still be sensitive to variations such as rotations or scale changes, even though they are robust to small translations.