Search Results for author:
Found 13 papers, 4 papers with code
Seismic First Break Picking in a Higher Dimension Using Deep Graph Learning
Addressing this, we propose a novel approach using deep graph learning called DGL-FB, constructing a large graph to efficiently extract information.
Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation Purification
However, the present few-shot NER models assume that the labeled data are all clean without noise or outliers, and there are few works focusing on the robustness of the cross-domain transfer learning ability to textual adversarial attacks in Few-shot NER.
Seismic Data Interpolation based on Denoising Diffusion Implicit Models with Resampling
The model training is established on the denoising diffusion probabilistic model, where U-Net is equipped with the multi-head self-attention to match the noise in each step.
UPNet: Uncertainty-based Picking Deep Learning Network for Robust First Break Picking
Many deep neural networks (DNNs)-based automatic picking methods have been proposed to accelerate this processing.
MSSPN: Automatic First Arrival Picking using Multi-Stage Segmentation Picking Network
Besides, since the gather data is a set of signals which are greatly different from the natural images, it is difficult for the current method to solve the FAT picking problem in case of a low Signal to Noise Ratio (SNR).
Deep Convolutional Sparse Coding Network for Pansharpening with Guidance of Side Information
Pansharpening is a fundamental issue in remote sensing field.
Deep Gradient Projection Networks for Pan-sharpening
Specifically, two optimization problems regularized by the deep prior are formulated, and they are separately responsible for the generative models for panchromatic images and low resolution multispectral images.
FGF-GAN: A Lightweight Generative Adversarial Network for Pansharpening via Fast Guided Filter
In addition, the latent information of features can be preserved effectively through adversarial training.
Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy
According to the idea that each local region in the fused image should be similar to the sharpest one among source images, this paper presents an optimization-based approach to reduce defocus spread effects.
When Image Decomposition Meets Deep Learning: A Novel Infrared and Visible Image Fusion Method
The core idea is that the encoder decomposes an image into base and detail feature maps with low- and high-frequency information, respectively, and that the decoder is responsible for the original image reconstruction.
Efficient and Model-Based Infrared and Visible Image Fusion Via Algorithm Unrolling
The proposed AUIF model starts with the iterative formulas of two traditional optimization models, which are established to accomplish two-scale decomposition, i. e., separating low-frequency base information and high-frequency detail information from source images.
Infrared And Visible Image Fusion
Rolling Shutter Correction
MFFW: A new dataset for multi-focus image fusion
It is found that current methods are evaluated on simulated image sets or Lytro dataset.
Pruning variable selection ensembles
In the context of variable selection, ensemble learning has gained increasing interest due to its great potential to improve selection accuracy and to reduce false discovery rate.
