How do Fully Convolutional Networks Upsample Their Coarse Output?

When dealing with tasks such as semantic segmentation, fully convolutional networks (FCNs) often generate coarse outputs that need to be upsampled to match the original input dimensions. FCNs employ various techniques to achieve this upscaling effectively. In this article, we will explore some of the most common methods used for upsampling in FCNs, including Transposed Convolutions, Nearest Neighbor Upsampling, Bilinear or Bicubic Interpolation, Skip Connections and Feature Fusion, and PixelShuffle/Sub-pixel Convolution.

Transposed Convolutions (Deconvolutions)

One of the most popular methods for upsampling in FCNs, transposed convolution layers perform the inverse operation of standard convolution, spreading out the input feature map to a larger output feature map. This method is widely used due to its efficiency and effectiveness in expanding the spatial dimensions of feature maps while retaining important features.

PyTorch Implementation Example

Here’s a simple example of how to implement a transposed convolution layer in PyTorch:

import torchimport torch.nn as nn# Define a simple transposed convolution layerclass UpsampleLayer():    def __init__(self, in_channels, out_channels, kernel_size2, stride2):        super(UpsampleLayer, self).__init__()          (in_channels, out_channels, kernel_size, stride)    def forward(self, x):        return (x)# Example usageinput_tensor  torch.randn(1, 16, 8, 8)  # Batch size 1, 16 channels, 8x8 feature mapupsample_layer  UpsampleLayer(16, 8)output_tensor  upsample_layer(input_tensor)print(output_tensor)  # Output shape will be 1 x 8 x 16 x 16

Nearest Neighbor Upsampling

A simpler yet computationally efficient method for upsampling, Nearest Neighbor Upsampling duplicates the values of the input feature map to create a larger output feature map. While it retains sharp edges, it might not produce visually smooth results. For example, if you want to upsample by a factor of 2, each pixel in the input is repeated in a 2x2 block in the output.

Bilinear or Bicubic Interpolation

For more visually appealing outputs, interpolation methods such as bilinear and bicubic can be used to resize feature maps by estimating the values of new pixels based on the values of surrounding pixels. Bilinear interpolation uses a linear approach in two dimensions, while bicubic interpolation uses cubic polynomials to achieve smoother results. These methods can lead to more visually appealing outputs compared to nearest neighbor upsampling.

Skip Connections and Feature Fusion

In many FCN architectures, such as U-Net, skip connections are used to combine high-resolution features from earlier layers with upsampled features from later layers. This helps in retaining fine details that may be lost during downscaling and upscaling processes.

PixelShuffle/Sub-pixel Convolution

This method rearranges the pixels of a low-resolution feature map into a higher-resolution output. It involves reshaping the input tensor and can effectively increase the spatial resolution while maintaining the original information. This technique is particularly useful for upscaling feature maps to their original dimensions.

Overall, FCNs employ various upsampling techniques to recover spatial dimensions, each with its strengths and weaknesses depending on the specific application and desired output quality. Understanding these techniques is crucial for designing robust FCN architectures that can effectively handle tasks such as semantic segmentation and other spatially intensive applications.