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Found 18 papers, 8 papers with code
HQ-VAE: Hierarchical Discrete Representation Learning with Variational Bayes
Vector quantization (VQ) is a technique to deterministically learn features with discrete codebook representations.
Manifold Preserving Guided Diffusion
Despite the recent advancements, conditional image generation still faces challenges of cost, generalizability, and the need for task-specific training.
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.
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
VRDMG: Vocal Restoration via Diffusion Posterior Sampling with Multiple Guidance
Restoring degraded music signals is essential to enhance audio quality for downstream music manipulation.
The Sound Demixing Challenge 2023 $\unicode{x2013}$ Music Demixing Track
We propose a formalization of the errors that can occur in the design of a training dataset for MSS systems and introduce two new datasets that simulate such errors: SDXDB23_LabelNoise and SDXDB23_Bleeding.
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.
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.
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.
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.
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).
Robust Vector Quantized-Variational Autoencoder
We experimentally demonstrate that RVQ-VAE is able to generate examples from inliers even if a large portion of the training data points are corrupted.
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.
Unlocking Inverse Problems Using Deep Learning: Breaking Symmetries in Phase Retrieval
In many physical systems, inputs related by intrinsic system symmetries generate the same output.
Novelty Detection via Robust Variational Autoencoding
We establish both robustness to outliers and suitability to low-rank modeling of the Wasserstein metric as opposed to the KL divergence.
Inverse Problems, Deep Learning, and Symmetry Breaking
In many physical systems, inputs related by intrinsic system symmetries are mapped to the same output.
Robust Subspace Recovery Layer for Unsupervised Anomaly Detection
The encoder maps the data into a latent space, from which the RSR layer extracts the subspace.
Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly
Unsupervised Anomaly Detection with Specified Settings -- 10% anomaly
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