CNNs are deep learning networks created specifically for structured data – more specifically, for images and videos. These networks work like human vision since they try to extract patterns and structures in data. Subsequently, different types of CNN architectures were introduced as follows Other Years Specific:Classification: Its includes image recognition, face detection etc Other Years Specific:Detection: Its include object detection Other Years Specific:Segmentation: Its includes image segmentation. The knowledge of several CNNs is a necessity when choosing the right model for the specific task.
Standard CNN
Standard CNNs are the basic model for image-related jobs. These models include convolution layers as the feature extractors, down sampling layers as the dimensionality reducers, and classification layers being the fully connected layers. Standard CNNs are usable and generalizable for a range of applications within image classification, detection of objects, and primary computer vision tasks. Because they are simple, they can act as the base for several applications.
FCNs, which stands for Fully Convolutional Networks.
FCNs represent an evolution of the basic CNNs, for predicting at the pixel level. While the standard CNN has fully connected layers, an FCN replaces them with convolutional layers so as to allow the network to generate spatial outputs. This architecture is especially useful for segmentation issues, which require the categorization of each pixel, for example, to detect tumors in mammography or an object’s position for an autonomous vehicle.
Recurrent CNNs
Recurrent Convolutional Neural Networks are an improvement over the CNN and the Recurrent Neural Networks hybrid which can analyze periodic image data. These architectures are frequently used for tasks like video analysis and action recognition where the temporal structure of data is as valuable as the spatial structure. The Recurrent CNNs make it possible to examine more dynamic television content and therefore, can be used to perform video analysis.
Residual Networks (ResNets)
Residual Networks also overcome the vanishing gradient problem which is found in deep networks. This deep learning model introduces shortcut connection that eliminates one or two layers in order to train very deep models efficiently. These architectures have established themselves as very efficient in deep network tasks mainly in complicated image classification and other superior computer vision problems.
Application of Generative Adversarial Networks combined with CNNs
GANs incorporate CNN in their generator and discriminator to generate realistic images G. GANs utilize two networks: One produces fake data, the other can determine its credibility. The integration of CNNs in GANs strengthens its capacity to generate natural visual samples and also can be applied to image synthesis and artistry.
U-Net Architecture
U-Net is CNN designed for biomedical image segmentation. It has an encoder–decoder architecture, where skip connections connect similar layers with each other. Most of these skip connections have the effect of preserving spatial information during feature extraction and reconstruction. U-Net has become very popular for medical image processing tasks because of its capacity to work with small datasets and for offering accurate segmentations.
MobileNet
MobileNet is considered to be light CNN serving the purpose for mobiles and low power devices such as micro controllers. By the depth-wise separable convolutions, it tremendously reduces the computational measures but increases the accuracy. MobileNet is best suitable for use in applications where computational power is a constraint – a characteristic seen in devices such as smartphones and IoT devices used for real-time image-recognition.
The proposed model is known as Feature Pyramid Networks or FPNs .
From the Feature Pyramid Networks, one gets the understanding that the multi-scale feature maps can improve the outcome of an object detection model. Here, FPNs enhance the scale-invariant features by merging low and high-level features hence increasing the aspect ratio in order to detect objects of different sizes. This type of architecture is common in various real-life applications: the inclusion of images and the locational detection of objects such as a car on the road.
Applications of CNN Types
Standard craftsmanship of the CNN structures presents separable types for each form of problem as reactions to the different necessities of image and video analysis. Although regular CNNs are general and can be used in many applications, there are specific architectures such as U-Net and MobileNet for specific issues. Choosing the proper type of CNN means considering the amount and dimensionality of data and available computational power and specificity of application.
Conclusion
The availability of different types of CNN proves the appropriateness of these networks to handle real world problems. Originally starting with simple image classification tasks, CNNs are now in the field of video analysis, and even medical applications. A further understanding of such architectures enables the successful application of CNNs for various tasks.