Different Types of CNN Architecture

Different Types of CNN Architecture

Introduction

Convolutional Neural Networks (CNNs) have significantly advanced the field of deep learning, particularly in domains like computer vision, speech recognition, and natural language processing. Designed to handle grid-like data structures such as images, CNNs are composed of layers that learn to identify various patterns and features in data, such as edges, shapes, or textures.

The main parts of CNNs are convolutional layers, pooling layers, and activation functions. After that, fully connected layers are also used. All these work together in steps so the network can learn better and more complex things from the data. To make the model perform better, things like dropout and batch normalization are also used.

In this article by Updategadh, we explained the main parts of CNN architecture in simple way and also shared some popular CNN models that helped deep learning grow.

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Core Components of CNN Architecture

1. Input Layer

This layer receives the input data, typically an image represented in height × width × channels format (e.g., a 64×64 RGB image has 3 channels).

2. Convolutional Layers

These are the primary building blocks of CNNs. To extract features from the input data, each layer uses a collection of learnable filters, or kernels. Patterns like edges, gradients, and textures are detected with the aid of the filters.

3. Activation Functions

Non-linear functions like ReLU (Rectified Linear Unit) are applied after convolution to introduce non-linearity, allowing the model to learn more complex representations.

4. Pooling Layers

Pooling layers reduce the spatial dimensions of the data, making computation more efficient and helping control overfitting. There are two popular kinds: max pooling and average pooling.

5. Flattening

Multi-dimensional data from earlier levels is flattened into a one-dimensional vector before being passed to fully connected layers.

6. Fully Connected Layers

These layers (also called dense layers) connect every neuron in one layer to every neuron in the next. They supply the output layer the combined learnt characteristics.

7. Output Layer

The final layer uses an activation function like softmax (for classification) or linear (for regression) to produce the final prediction.

Popular CNN Architectures

LeNet-5

• Introduced by: LeCun Yann in the 1990s
• Use case: Handwritten digit recognition (e.g., MNIST dataset)
• Architecture: Consists of convolutional layers, fully connected layers, and average pooling.
• Highlights: Tanh activation and the SGD optimiser were utilised by one of the first CNNs.

AlexNet

• Introduced by: Geoffrey Hinton, Ilya Sutskever, and Alex Krizhevsky (2012)
• Use case: Large-scale image classification (ImageNet)
• Architecture: Utilising ReLU, dropout, and data augmentation, it is more profound than LeNet.
• Highlights: Won the 2012 ImageNet challenge; marked a breakthrough for deep learning in vision tasks

VGGNet (VGG16, VGG19)

• Introduced by: Oxford University’s Visual Geometry Group (2014)
• Use case: Object classification
• Architecture: 16 or 19-layer deep network that employs tiny (3×3) filters
• Highlights: Known for simplicity and depth; excellent performance on ImageNet

GoogleNet (Inception v1)

• Introduced by: Google, Szegedy et al. (2014)
• Use case: Efficient deep learning on limited resources
• Architecture: Introduced inception modules to capture multi-scale features
• Highlights: Employs auxiliary classifiers to address the diminishing gradient and uses fewer parameters.

ResNet (Residual Network)

• Introduced by: Kaiming He et al. (2015)
• Use case: Very deep network training
• Architecture: Includes skip (residual) connections that bypass layers
• Highlights: Solves degradation problem; enables training of networks with 100+ layers

DenseNet

• Introduced by: Gao Huang et al. (2016)
• Use case: Efficient and accurate image classification
• Architecture: Each layer connects to every other layer in a feed-forward fashion
• Highlights: Encourages feature reuse; enhances gradient flow

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Conclusion

CNNs have helped machines to “see” and understand things using data. From basic models like LeNet-5 to advanced ones like ResNet and DenseNet, each one has its own benefits depending on the task and system. As technology is growing, new types of CNN models are also coming, making AI even more powerful and smarter than before.

For more deep learning guides and updates, keep exploring Updategadh.

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