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Found 5 papers, 3 papers with code
Discovering Class-Specific GAN Controls for Semantic Image Synthesis
Prior work has extensively studied the latent space structure of GANs for unconditional image synthesis, enabling global editing of generated images by the unsupervised discovery of interpretable latent directions.
You Only Need Adversarial Supervision for Semantic Image Synthesis
By providing stronger supervision to the discriminator as well as to the generator through spatially- and semantically-aware discriminator feedback, we are able to synthesize images of higher fidelity with better alignment to their input label maps, making the use of the perceptual loss superfluous.
A U-Net Based Discriminator for Generative Adversarial Networks
The novel discriminator improves over the state of the art in terms of the standard distribution and image quality metrics, enabling the generator to synthesize images with varying structure, appearance and levels of detail, maintaining global and local realism.
Ranked #1 on
Image Generation
on CelebA 128x128
Cross-Linked Variational Autoencoders for Generalized Zero-Shot Learning
While following the same direction, we also take artificial feature generation one step further and propose a model where a shared latent space of image features and class embeddings is learned by aligned variational autoencoders, for the purpose of generating latent features to train a softmax classifier.
Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
Many approaches in generalized zero-shot learning rely on cross-modal mapping between the image feature space and the class embedding space.
Ranked #2 on
Generalized Few-Shot Learning
on AwA2
