Search Results for author:
Found 28 papers, 9 papers with code
MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation
This approach effectively synergizes reference image and text prompt information to produce valuable image features, facilitating an image diffusion model.
Online Differentially Private Synthetic Data Generation
We present a polynomial-time algorithm for online differentially private synthetic data generation.
MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion
In this work, we propose MagicPose, a diffusion-based model for 2D human pose and facial expression retargeting.
Spectral gap-based deterministic tensor completion
However, numerical experiments illustrate the dependence of the reconstruction error on the spectral gap for the practical max-quasinorm, ridge penalty, and Poisson loss minimization algorithms.
Differentially Private Low-dimensional Synthetic Data from High-dimensional Datasets
Differentially private synthetic data provide a powerful mechanism to enable data analysis while protecting sensitive information about individuals.
Shifted Diffusion for Text-to-image Generation
Unlike the baseline diffusion model used in DALL-E 2, our method seamlessly encodes prior knowledge of the pre-trained CLIP model in its diffusion process by designing a new initialization distribution and a new transition step of the diffusion.
Ranked #3 on
Text-to-Image Generation
on Multi-Modal-CelebA-HQ
MagicVideo: Efficient Video Generation With Latent Diffusion Models
In specific, unlike existing works that directly train video models in the RGB space, we use a pre-trained VAE to map video clips into a low-dimensional latent space and learn the distribution of videos' latent codes via a diffusion model.
Ranked #10 on
Text-to-Video Generation
on MSR-VTT
Overparameterized random feature regression with nearly orthogonal data
Our analysis shows high-probability non-asymptotic concentration results for the training errors, cross-validations, and generalization errors of RFRR centered around their respective values for a kernel ridge regression (KRR).
Sparse random hypergraphs: Non-backtracking spectra and community detection
We consider the community detection problem in a sparse $q$-uniform hypergraph $G$, assuming that $G$ is generated according to the Hypergraph Stochastic Block Model (HSBM).
Partial recovery and weak consistency in the non-uniform hypergraph Stochastic Block Model
When the random hypergraph has bounded expected degrees, we provide a spectral algorithm that outputs a partition with at least a $\gamma$ fraction of the vertices classified correctly, where $\gamma\in (0. 5, 1)$ depends on the signal-to-noise ratio (SNR) of the model.
PIVQGAN: Posture and Identity Disentangled Image-to-Image Translation via Vector Quantization
The VQSN module facilitates a more delicate separation of posture and identity, while the training scheme ensures the VQSN module learns the pose-related representations.
Deformed semicircle law and concentration of nonlinear random matrices for ultra-wide neural networks
As an application, we show that random feature regression induced by the empirical kernel achieves the same asymptotic performance as its limiting kernel regression under the ultra-wide regime.
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
Training Generative Adversarial Networks (GAN) on high-fidelity images usually requires large-scale GPU-clusters and a vast number of training images.
Ranked #2 on
Image Generation
on ADE-Indoor
Self-Supervised Sketch-to-Image Synthesis
Moreover, with the proposed sketch generator, the model shows a promising performance on style mixing and style transfer, which require synthesized images to be both style-consistent and semantically meaningful.
Global eigenvalue fluctuations of random biregular bipartite graphs
We compute the eigenvalue fluctuations of uniformly distributed random biregular bipartite graphs with fixed and growing degrees for a large class of analytic functions.
Probability Combinatorics
TIME: Text and Image Mutual-Translation Adversarial Networks
Focusing on text-to-image (T2I) generation, we propose Text and Image Mutual-Translation Adversarial Networks (TIME), a lightweight but effective model that jointly learns a T2I generator G and an image captioning discriminator D under the Generative Adversarial Network framework.
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation
We propose a sequential variational autoencoder to learn disentangled representations of sequential data (e. g., videos and audios) under self-supervision.
Federated Adversarial Domain Adaptation
In this work, we present a principled approach to the problem of federated domain adaptation, which aims to align the representations learned among the different nodes with the data distribution of the target node.
Deterministic tensor completion with hypergraph expanders
We provide a novel analysis of low-rank tensor completion based on hypergraph expanders.
OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization
Exploring the potential of GANs for unsupervised disentanglement learning, this paper proposes a novel GAN-based disentanglement framework with One-Hot Sampling and Orthogonal Regularization (OOGAN).
Learning Feature-to-Feature Translator by Alternating Back-Propagation for Generative Zero-Shot Learning
We investigate learning feature-to-feature translator networks by alternating back-propagation as a general-purpose solution to zero-shot learning (ZSL) problems.
Community detection in the sparse hypergraph stochastic block model
We consider the community detection problem in sparse random hypergraphs.
Semantic-Guided Multi-Attention Localization for Zero-Shot Learning
Zero-shot learning extends the conventional object classification to the unseen class recognition by introducing semantic representations of classes.
Exact Recovery in the Hypergraph Stochastic Block Model: a Spectral Algorithm
We consider the exact recovery problem in the hypergraph stochastic block model (HSBM) with $k$ blocks of equal size.
CU-Net: Coupled U-Nets
We design a new connectivity pattern for the U-Net architecture.
Ranked #30 on
Pose Estimation
on MPII Human Pose
A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts
Most existing zero-shot learning methods consider the problem as a visual semantic embedding one.
Link the head to the "beak": Zero Shot Learning from Noisy Text Description at Part Precision
We propose a learning framework that is able to connect text terms to its relevant parts and suppress connections to non-visual text terms without any part-text annotations.
A Multilayer-Based Framework for Online Background Subtraction with Freely Moving Cameras
The exponentially increasing use of moving platforms for video capture introduces the urgent need to develop the general background subtraction algorithms with the capability to deal with the moving background.
