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Found 18 papers, 7 papers with code
Class-Balanced and Reinforced Active Learning on Graphs
It learns an optimal policy to acquire class-balanced and informative nodes for annotation, maximizing the performance of GNNs trained with selected labeled nodes.
In-Domain GAN Inversion for Faithful Reconstruction and Editability
This work fills in this gap by proposing in-domain GAN inversion, which consists of a domain-guided encoder and a domain-regularized optimizer, to regularize the inverted code in the native latent space of the pre-trained GAN model.
Exploring Sparse MoE in GANs for Text-conditioned Image Synthesis
Due to the difficulty in scaling up, generative adversarial networks (GANs) seem to be falling from grace on the task of text-conditioned image synthesis.
GH-Feat: Learning Versatile Generative Hierarchical Features from GANs
In this work we investigate that such a generative feature learned from image synthesis exhibits great potentials in solving a wide range of computer vision tasks, including both generative ones and more importantly discriminative ones.
LinkGAN: Linking GAN Latents to Pixels for Controllable Image Synthesis
This work presents an easy-to-use regularizer for GAN training, which helps explicitly link some axes of the latent space to a set of pixels in the synthesized image.
Interpreting Class Conditional GANs with Channel Awareness
To describe such a phenomenon, we propose channel awareness, which quantitatively characterizes how a single channel contributes to the final synthesis.
High-fidelity GAN Inversion with Padding Space
In this work, we propose to involve the padding space of the generator to complement the latent space with spatial information.
Region-Based Semantic Factorization in GANs
Despite the rapid advancement of semantic discovery in the latent space of Generative Adversarial Networks (GANs), existing approaches either are limited to finding global attributes or rely on a number of segmentation masks to identify local attributes.
3D-Aware Indoor Scene Synthesis with Depth Priors
In this way, the discriminator can take the spatial arrangement into account and advise the generator to learn an appropriate depth condition.
One-Shot Generative Domain Adaptation
We then equip the well-learned discriminator backbone with an attribute classifier to ensure that the generator captures the appropriate characters from the reference.
Low-Rank Subspaces in GANs
Concretely, given an arbitrary image and a region of interest (e. g., eyes of face images), we manage to relate the latent space to the image region with the Jacobian matrix and then use low-rank factorization to discover steerable latent subspaces.
Generative Hierarchical Features from Synthesizing Images
Generative Adversarial Networks (GANs) have recently advanced image synthesis by learning the underlying distribution of the observed data.
In-Domain GAN Inversion for Real Image Editing
A common practice of feeding a real image to a trained GAN generator is to invert it back to a latent code.
Latent Variables on Spheres for Autoencoders in High Dimensions
Variational Auto-Encoder (VAE) has been widely applied as a fundamental generative model in machine learning.
Latent Variables on Spheres for Sampling and Inference
Variational inference is a fundamental problem in Variational AutoEncoder (VAE).
LIA: Latently Invertible Autoencoder with Adversarial Learning
A two-stage stochasticity-free training scheme is designed to train LIA via adversarial learning, in the sense that the decoder of LIA is first trained as a standard GAN with the invertible network and then the partial encoder is learned from an autoencoder by detaching the invertible network from LIA.
Curriculum Learning for Deep Generative Models with Clustering
The experiments on cat and human-face data validate that our algorithm is able to learn the optimal generative models (e. g. ProGAN) with respect to specified quality metrics for noisy data.
Disentangled Inference for GANs with Latently Invertible Autoencoder
In this paper, we show that the entanglement of the latent space for the VAE/GAN framework poses the main challenge for encoder learning.
