Semantic Pyramid for Image Generation

This video explores a new GAN model for generating images by conditioning them on features from pre-trained image classifiers! This is really interesting for visualizing what is contained in pre-trained image classifiers as well as controllable image editing. The authors also show that this can be used for semantic image composition such as copying a tree and pasting it into a snow landscape or image relabeling by changing the embedded logit from the pre-trained classifier to produce an image of a new class while retaining as much of the original image as possible. Thanks for watching! Please Subscribe! Paper Links: Semantic Pyramid for Image Generation: https://ift.tt/396KOsF Corresponding Github Page: https://ift.tt/2IZbAbw Neural Style Transfer: https://ift.tt/2kNfxFU Zoom In: An Introduction to Circuits: https://ift.tt/39EOZgn EfficientDet: https://ift.tt/2xQJ7m7 Generative Teaching Networks: https://ift.tt/2w8Yd64 DermGAN: https://ift.tt/2vwCAMt Classification Accuracy Score for Conditional Generative Models: https://ift.tt/2U1fmaU GauGAN: https://ift.tt/2CsbLsZ SinGAN: https://ift.tt/2WnPSG5 StyleGAN2 Distillation: https://ift.tt/2IDaKRK Semi-Supervised StyleGAN for Disentanglement Learning: https://ift.tt/3b5VfxN Thanks for watching! Please Subscribe!