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Found 15 papers, 8 papers with code
BigVSAN: Enhancing GAN-based Neural Vocoders with Slicing Adversarial Network
In the literature, it has been demonstrated that slicing adversarial network (SAN), an improved GAN training framework that can find the optimal projection, is effective in the image generation task.
Ranked #2 on
Speech Synthesis
on LibriTTS
SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization
In this paper, we propose a new training scheme that extends the standard VAE via novel stochastic dequantization and quantization, called stochastically quantized variational autoencoder (SQ-VAE).
Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion
Consistency Models (CM) (Song et al., 2023) accelerate score-based diffusion model sampling at the cost of sample quality but lack a natural way to trade-off quality for speed.
Ranked #2 on
Image Generation
on CIFAR-10
Automatic Piano Transcription with Hierarchical Frequency-Time Transformer
This is especially helpful when determining the precise onset and offset for each note in the polyphonic piano content.
Diffiner: A Versatile Diffusion-based Generative Refiner for Speech Enhancement
Although deep neural network (DNN)-based speech enhancement (SE) methods outperform the previous non-DNN-based ones, they often degrade the perceptual quality of generated outputs.
SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer
Generative adversarial networks (GANs) learn a target probability distribution by optimizing a generator and a discriminator with minimax objectives.
Ranked #1 on
Image Generation
on FFHQ 1024 x 1024
GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration
Pre-trained diffusion models have been successfully used as priors in a variety of linear inverse problems, where the goal is to reconstruct a signal from noisy linear measurements.
FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation
Score-based generative models (SGMs) learn a family of noise-conditional score functions corresponding to the data density perturbed with increasingly large amounts of noise.
AR-ELBO: Preventing Posterior Collapse Induced by Oversmoothing in Gaussian VAE
Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon that the learned latent space becomes uninformative.
Preventing Oversmoothing in VAE via Generalized Variance Parameterization
Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon in which the learned latent space becomes uninformative.
Unsupervised vocal dereverberation with diffusion-based generative models
Removing reverb from reverberant music is a necessary technique to clean up audio for downstream music manipulations.
On the Equivalence of Consistency-Type Models: Consistency Models, Consistent Diffusion Models, and Fokker-Planck Regularization
The emergence of various notions of ``consistency'' in diffusion models has garnered considerable attention and helped achieve improved sample quality, likelihood estimation, and accelerated sampling.
On the Language Encoder of Contrastive Cross-modal Models
Contrastive cross-modal models such as CLIP and CLAP aid various vision-language (VL) and audio-language (AL) tasks.
Manifold Preserving Guided Diffusion
Despite the recent advancements, conditional image generation still faces challenges of cost, generalizability, and the need for task-specific training.
HQ-VAE: Hierarchical Discrete Representation Learning with Variational Bayes
Vector quantization (VQ) is a technique to deterministically learn features with discrete codebook representations.
