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Found 30 papers, 9 papers with code
BELM: Bidirectional Explicit Linear Multi-step Sampler for Exact Inversion in Diffusion Models
In this paper, we introduce a generic formulation, \emph{Bidirectional Explicit Linear Multi-step} (BELM) samplers, of the exact inversion samplers, which includes all previously proposed heuristic exact inversion samplers as special cases.
LW2G: Learning Whether to Grow for Prompt-based Continual Learning
To promote cross-task knowledge facilitation and form an effective and efficient prompt sets pool, we propose a plug-in module in the former stage to \textbf{Learn Whether to Grow (LW2G)} based on the disparities between tasks.
TextToucher: Fine-Grained Text-to-Touch Generation
We model these granularities of information through text descriptions and propose a fine-grained Text-to-Touch generation method (TextToucher) to generate high-quality tactile samples.
DriveDiTFit: Fine-tuning Diffusion Transformers for Autonomous Driving
Such datasets are expected to cover various driving scenarios with adverse weather, lighting conditions and diverse moving objects.
PECTP: Parameter-Efficient Cross-Task Prompts for Incremental Vision Transformer
Prompt-fixed methods only learn a single set of prompts on one of the incremental tasks and can not handle all the incremental tasks effectively.
Neural Sinkhorn Gradient Flow
We utilize the velocity field matching training scheme in NSGF, which only requires samples from the source and target distribution to compute an empirical velocity field approximation.
GAD-PVI: A General Accelerated Dynamic-Weight Particle-Based Variational Inference Framework
Particle-based Variational Inference (ParVI) methods approximate the target distribution by iteratively evolving finite weighted particle systems.
Towards Optimal Randomized Strategies in Adversarial Example Game
The vulnerability of deep neural network models to adversarial example attacks is a practical challenge in many artificial intelligence applications.
PACER: A Fully Push-forward-based Distributional Reinforcement Learning Algorithm
As a result, PACER fully utilizes the modeling capability of the push-forward operator and is able to explore a broader class of the policy space, compared with limited policy classes used in existing distributional actor critic algorithms (i. e. Gaussians).
An Asynchronous Decentralized Algorithm for Wasserstein Barycenter Problem
Wasserstein Barycenter Problem (WBP) has recently received much attention in the field of artificial intelligence.
Accelerated Doubly Stochastic Gradient Algorithm for Large-scale Empirical Risk Minimization
Nowadays, algorithms with fast convergence, small memory footprints, and low per-iteration complexity are particularly favorable for artificial intelligence applications.
ClassPruning: Speed Up Image Restoration Networks by Dynamic N:M Pruning
Image restoration tasks have achieved tremendous performance improvements with the rapid advancement of deep neural networks.
Rethinking Performance Gains in Image Dehazing Networks
Image dehazing is an active topic in low-level vision, and many image dehazing networks have been proposed with the rapid development of deep learning.
Ranked #2 on
Image Dehazing
on RS-Haze
Vision Transformers for Single Image Dehazing
Image dehazing is a representative low-level vision task that estimates latent haze-free images from hazy images.
Ranked #1 on
Image Dehazing
on RS-Haze
Multi-Curve Translator for High-Resolution Photorealistic Image Translation
The dominant image-to-image translation methods are based on fully convolutional networks, which extract and translate an image's features and then reconstruct the image.
SIGMA: A Structural Inconsistency Reducing Graph Matching Algorithm
In this paper, we propose a novel criterion to measure the graph matching accuracy, structural inconsistency (SI), which is defined based on the network topological structure.
DPVI: A Dynamic-Weight Particle-Based Variational Inference Framework
We develop a general Dynamic-weight Particle-based Variational Inference (DPVI) framework according to a novel continuous composite flow, which evolves the positions and weights of particles simultaneously.
Approximating Optimal Transport via Low-rank and Sparse Factorization
Optimal transport (OT) naturally arises in a wide range of machine learning applications but may often become the computational bottleneck.
StarEnhancer: Learning Real-Time and Style-Aware Image Enhancement
To make the method more practical, we propose a well-designed enhancer that can process a 4K-resolution image over 200 FPS but surpasses the contemporaneous single style image enhancement methods in terms of PSNR, SSIM, and LPIPS.
CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems
We establish theoretical guarantees of CDMA under different choices of hyperparameters and conduct experiments on AUC maximization, robust adversarial network training, and GAN training tasks.
Generative Actor-Critic: An Off-policy Algorithm Using the Push-forward Model
Model-free deep reinforcement learning has achieved great success in many domains, such as video games, recommendation systems and robotic control tasks.
Partial Gromov-Wasserstein Learning for Partial Graph Matching
Graph matching finds the correspondence of nodes across two graphs and is a basic task in graph-based machine learning.
Aggregated Gradient Langevin Dynamics
In this paper, we explore a general Aggregated Gradient Langevin Dynamics framework (AGLD) for the Markov Chain Monte Carlo (MCMC) sampling.
Efficient Projection-Free Online Methods with Stochastic Recursive Gradient
This paper focuses on projection-free methods for solving smooth Online Convex Optimization (OCO) problems.
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Recently, the decentralized optimization problem is attracting growing attention.
Accelerated Variance Reduced Block Coordinate Descent
Algorithms with fast convergence, small number of data access, and low per-iteration complexity are particularly favorable in the big data era, due to the demand for obtaining \emph{highly accurate solutions} to problems with \emph{a large number of samples} in \emph{ultra-high} dimensional space.
Riemannian Tensor Completion with Side Information
By restricting the iterate on a nonlinear manifold, the recently proposed Riemannian optimization methods prove to be both efficient and effective in low rank tensor completion problems.
Co-interest Person Detection from Multiple Wearable Camera Videos
Wearable cameras, such as Google Glass and Go Pro, enable video data collection over larger areas and from different views.
LooseCut: Interactive Image Segmentation with Loosely Bounded Boxes
In this paper, we develop a new LooseCut algorithm that can handle cases where the input bounding box only loosely covers the foreground object.
A Nonconvex Approach for Structured Sparse Learning
In this paper we focus on the $\ell_q$-analysis optimization problem for structured sparse learning ($0< q \leq 1$).
