CNN convolution layers
Convolutional layers help in mapping features. What are the benefits of having multiple convolutional layers, and what will each convolutional layer do?
1 answers ( 0 marked as helpful)
Hi,
Well, just as with all neural networks, each additional convolutional layer increases the network's complexity and allows it to detect more details.
Typically, early convolutional layers focus on simple features, such as edges and colors. Deeper layers, use these features to detect more complex patterns.
E.g. when considering an image of the night sky, early layers may detect a circle and white, while deeper layers may determine whether the white dot is a star, the moon or a lamp in the distance.
At the end of the day, however, each network is different and CNNs are often treated as a black box model, meaning it's difficult to understand the precise workings of each part. Often, you have to analyze each specific network trained on a specific dataset, to understand how it functions.
Hope this helps!
Best,
Nikola, 365 Team
Well, just as with all neural networks, each additional convolutional layer increases the network's complexity and allows it to detect more details.
Typically, early convolutional layers focus on simple features, such as edges and colors. Deeper layers, use these features to detect more complex patterns.
E.g. when considering an image of the night sky, early layers may detect a circle and white, while deeper layers may determine whether the white dot is a star, the moon or a lamp in the distance.
At the end of the day, however, each network is different and CNNs are often treated as a black box model, meaning it's difficult to understand the precise workings of each part. Often, you have to analyze each specific network trained on a specific dataset, to understand how it functions.
Hope this helps!
Best,
Nikola, 365 Team