Summary

In this chapter, we have learned about the different variants of autoencoders: vanilla, convolutional, and variational. We also learned about how the number of units in the bottleneck layer influences the reconstructed image. Next, we learned about identifying images that are similar to a given image using the t-SNE technique. We learned that when we sample vectors, we cannot get realistic images, and by using variational autoencoders, we learned about generating new images by using a combination of reconstruction loss and KL divergence loss. Next, we learned how to perform an adversarial attack on images to modify the class of an image while not changing the perceptive content of the image. Finally, we learned about leveraging the combination of content loss and gram matrix-based style loss to optimize for content and style loss of images to come up with an image that is a combination of two input images. Finally, we learned about tweaking an autoencoder to swap two faces without...