VGG Loss is a content loss method for super-resolution and style transfer. It aims to be more similar to human perception than pixel-wise losses, making it a valuable tool for image reconstruction.
What is VGG Loss?
When creating high-resolution images or transferring styles between images, it is essential to consider content loss. Content loss is the difference between the reference image and the reconstructed image, and minimizing it leads to a better output.
VGG Loss is an alternative to pixel-wise losses, aiming to be closer to perceptual similarity. It uses the 19-layer VGG network, which is pre-trained on millions of images, as a basis for the loss function. Specifically, it considers the feature maps obtained by the ReLU activation layers of the VGG network.
With VGG Loss, the reconstructed image is compared to the reference image by computing the Euclidean distance between their feature representations. The distances between individual units are summed up to calculate the overall VGG loss.
How does VGG Loss work?
VGG Loss is designed to take into account the perceived similarity between two images. To do this, it uses the feature maps extracted from the VGG network. A feature map is a map of image gradients based on the convolutional filter applied to the image.
The VGG network is used in this case because it has strong feature extraction capabilities. It can recognize patterns in images and distinguish between different object categories. By comparing a reconstructed image to a reference image in terms of their feature maps, VGG Loss assesses how well the reconstructed image preserves the same perceived content as the reference image.
VGG Loss is computed by iterating through the feature maps of the reconstructed image and the reference image. The Euclidean distance between the two feature maps is computed for each pair of units. The distances are then summed up and weighed based on the dimensions of the feature maps. The overall VGG Loss is the summation of all these weighted distances.
Applications of VGG Loss
VGG Loss is widely used in image super-resolution and style transfer. Super-resolution aims to generate high-resolution images from low-resolution images by adding new details to the existing image. Style transfer involves merging the content of one image with the style of another.
In both cases, VGG Loss is used as a measure of the perceptual similarity between two images. By minimizing the VGG Loss, the generated images are made to look more natural and realistic.
VGG Loss is a content loss method that uses the features extracted from the VGG network to assess the similarity between two images. It is widely used in image super-resolution and style transfer and has been shown to produce more perceptually similar results than pixel-wise losses.
While VGG Loss comes at a cost of computational complexity, it has become an essential component of state-of-the-art super-resolution and style transfer networks.
