Convolutional Encoders
Get introduced to the world of convolution encoders and explore their crucial role in feature extraction for computer vision.
Let's refresh our understanding of how convolution works in computer vision encoders.
Understanding convolution encoders
The core concept of a convolution encoder, also known as a backbone or feature extractor, is to extract local and translation-invariant features. These features are local in the sense that they depend on a specific region of the image, defined by a kernel's scope. They are also translation-invariant, meaning they can identify the same feature even if it shifts within the image. This detection relies on the interaction between kernel weights and the image pixels or the feature map.
Feature extraction process
For instance, if we begin with an image that is, let's say,
Get hands-on with 1200+ tech skills courses.