Are you interested in understanding how machines can perceive the world around them? Well, Fully Convolutional Networks (FCNs) might be the answer to your questions. FCNs are an architecture used mainly for semantic segmentation. They have proven to be quite effective in image recognition and other machine learning applications which require machines to understand their surroundings and make decisions based on that.
The Anatomy of Fully Convolutional Networks (FCNs)
FCNs use solely locally connected layers such as convolution, pooling, and upsampling to understand and interpret images. Compared to other architectures which use dense layers, FCNs use fewer parameters to train their models, making the networks faster. On top of that, FCNs can work with variable image sizes as long as all the connections are local.
The network is made of two paths, the downsampling path and the upsampling path. The downsampling path allows the network to extract and interpret the context of the image, identifying key features that may be crucial for making decisions. The upsampling path, on the other hand, allows the network to perform localization, which is crucial for identifying the exact location and shape of the objects in the image.
Skip Connections in Fully Convolutional Networks
The downsampling path is critical for identifying the context of the image, but this comes at a price, losing information about the fine-grained spatial information in the image. To address this issue, FCNs employ skip connections to recover the lost information. The skip connections, unlike the downsampling path, use the shallower layers in the network to recover the spatial information.
In summary, Fully Convolutional Networks use layers such as convolution, pooling, and upsampling to create a neural network that understands images. They are fast, require fewer parameters to train, and work for variable image sizes. On top of that, FCNs employ skip connections to recover fine-grained spatial information that may be lost in the downsampling path.
