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Found 16 papers, 4 papers with code
RB-Modulation: Training-Free Personalization of Diffusion Models using Stochastic Optimal Control
Existing training-free approaches exhibit difficulties in (a) style extraction from reference images in the absence of additional style or content text descriptions, (b) unwanted content leakage from reference style images, and (c) effective composition of style and content.
Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion
To our best knowledge, this is the first work to offer an efficient second-order approximation in solving inverse problems using latent diffusion and editing real-world images with corruptions.
Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models
We present the first framework to solve linear inverse problems leveraging pre-trained latent diffusion models.
Beyond Uniform Smoothness: A Stopped Analysis of Adaptive SGD
Despite the richness, an emerging line of works achieves the $\widetilde{\mathcal{O}}(\frac{1}{\sqrt{T}})$ rate of convergence when the noise of the stochastic gradients is deterministically and uniformly bounded.
A Theoretical Justification for Image Inpainting using Denoising Diffusion Probabilistic Models
To the best of our knowledge, this is the first linear convergence result for a diffusion based image inpainting algorithm.
Hierarchical Sliced Wasserstein Distance
We explain the usage of these projections by introducing Hierarchical Radon Transform (HRT) which is constructed by applying Radon Transform variants recursively.
An Optimal Transport Perspective on Unpaired Image Super-Resolution
First, the learned SR map is always an optimal transport (OT) map.
Generative Modeling with Optimal Transport Maps
In particular, we consider denoising, colorization, and inpainting, where the optimality of the restoration map is a desired attribute, since the output (restored) image is expected to be close to the input (degraded) one.
Why Adversarial Interaction Creates Non-Homogeneous Patterns: A Pseudo-Reaction-Diffusion Model for Turing Instability
Interestingly, we observe Turing-like patterns in a system of neurons with adversarial interaction.
Understanding the Role of Adversarial Regularization in Supervised Learning
Despite numerous attempts sought to provide empirical evidence of adversarial regularization outperforming sole supervision, the theoretical understanding of such phenomena remains elusive.
Monte-Carlo Siamese Policy on Actor for Satellite Image Super Resolution
Guided by recent advances in super resolution, we propose a theoretical framework that leverages the benefits of supervised and reinforcement learning.
S2A: Wasserstein GAN with Spatio-Spectral Laplacian Attention for Multi-Spectral Band Synthesis
In this study, we intend to address synthesis of high resolution multi-spectral satellite imagery using adversarial learning.
Is Supervised Learning With Adversarial Features Provably Better Than Sole Supervision?
Second, we analyze how adversarial learning augmented with supervised signal mitigates this vanishing gradient issue.
Learning Rotation Adaptive Correlation Filters in Robust Visual Object Tracking
Correlation Filter (CF) trackers are one of the most widely used categories in tracking.
DeepSWIR: A Deep Learning Based Approach for the Synthesis of Short-Wave InfraRed Band using Multi-Sensor Concurrent Datasets
Convolutional Neural Network (CNN) is achieving remarkable progress in various computer vision tasks.
Rotation Adaptive Visual Object Tracking with Motion Consistency
Therefore, one of the major aspects of this paper is to investigate the outcome of rotation adaptiveness in visual object tracking.
