Search Results for author:
Found 16 papers, 6 papers with code
Improve Cross-domain Mixed Sampling with Guidance Training for Adaptive Segmentation
Typically, various prevailing methods baseline rely on constructing intermediate domains via cross-domain mixed sampling techniques to mitigate the performance decline caused by domain gaps.
Ranked #8 on
Domain Adaptation
on SYNTHIA-to-Cityscapes
Sparse Variational Student-t Processes
However, despite Student-t Processes having a similar computational complexity as Gaussian Processes, there has been limited emphasis on the sparse representation of this model.
Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processes
Furthermore, our method guarantees theoretically controlled prediction error for DGP models and demonstrates remarkable performance on various datasets.
Data-driven Modeling and Inference for Bayesian Gaussian Process ODEs via Double Normalizing Flows
Bayesian inference for these models has been extensively studied and applied in tasks such as time series prediction.
SciRE-Solver: Accelerating Diffusion Models Sampling by Score-integrand Solver with Recursive Difference
Based on the RD method and the truncated Taylor expansion of score-integrand, we propose SciRE-Solver with the convergence order guarantee for accelerating sampling of DMs.
Ranked #7 on
Image Generation
on CIFAR-10
Multi-scale Attentive Image De-raining Networks via Neural Architecture Search
The proposed method formulates a new multi-scale attention search space with multiple flexible modules that are favorite to the image de-raining task.
TO-FLOW: Efficient Continuous Normalizing Flows with Temporal Optimization adjoint with Moving Speed
In this paper, a temporal optimization is proposed by optimizing the evolutionary time for forward propagation of the neural ODE training.
Self-Verification in Image Denoising
Specifically, we treat the output of the network as a ``prior'' that we denoise again after ``re-noising''.
Measuring the rogue wave pattern triggered from Gaussian perturbations by deep learning
Weak Gaussian perturbations on a plane wave background could trigger lots of rogue waves, due to modulational instability.
Deep Learning for Scene Classification: A Survey
Scene classification, aiming at classifying a scene image to one of the predefined scene categories by comprehending the entire image, is a longstanding, fundamental and challenging problem in computer vision.
Scan-flood Fill(SCAFF): an Efficient Automatic Precise Region Filling Algorithm for Complicated Regions
Experimental results show that the proposed algorithm can generate precise masks that allow for various machine learning tasks such as supervised training.
Graphics
Ro-SOS: Metric Expression Network (MEnet) for Robust Salient Object Segmentation
In this paper, we propose an end-to-end generic salient object segmentation model called Metric Expression Network (MEnet) to deal with saliency detection with the tolerance of distortion.
Removing Rain From Single Images via a Deep Detail Network
We propose a new deep network architecture for removing rain streaks from individual images based on the deep convolutional neural network (CNN).
A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation
We propose a weighted variational model to estimate both the reflectance and the illumination from an observed image.
Saliency Detection with Spaces of Background-based Distribution
In this letter, an effective image saliency detection method is proposed by constructing some novel spaces to model the background and redefine the distance of the salient patches away from the background.
Pan-Sharpening With a Hyper-Laplacian Penalty
Our objective incorporates the L1/2-norm in a way that can leverage recent computationally efficient methods, and L1 for which the alternating direction method of multipliers can be used.
