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Found 14 papers, 7 papers with code
Stabilizing Sharpness-aware Minimization Through A Simple Renormalization Strategy
Recently, sharpness-aware minimization (SAM) has attracted a lot of attention because of its surprising effectiveness in improving generalization performance. However, training neural networks with SAM can be highly unstable since the loss does not decrease along the direction of the exact gradient at the current point, but instead follows the direction of a surrogate gradient evaluated at another point nearby.
Trajectory-dependent Generalization Bounds for Deep Neural Networks via Fractional Brownian Motion
In this study, we argue that the hypothesis set SGD explores is trajectory-dependent and thus may provide a tighter bound over its Rademacher complexity.
Understanding Short-Range Memory Effects in Deep Neural Networks
Instead, inspired by the short-range correlation emerging in the SGN series, we propose that SGD can be viewed as a discretization of an SDE driven by fractional Brownian motion (FBM).
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.
Domain Adaptive Object Detection via Feature Separation and Alignment
Notably, scale-space filtering is exploited to implement adaptive searching for regions to be aligned, and instance-level features in each region are refined to reduce redundancy and noise mentioned in the second issue.
MFIF-GAN: A New Generative Adversarial Network for Multi-Focus Image Fusion
Multi-Focus Image Fusion (MFIF) is a promising image enhancement technique to obtain all-in-focus images meeting visual needs and it is a precondition of other computer vision tasks.
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.
Deep Convolutional Sparse Coding Networks for Image Fusion
Image fusion is a significant problem in many fields including digital photography, computational imaging and remote sensing, to name but a few.
Infrared And Visible Image Fusion
Multi-Exposure Image Fusion
Bayesian Fusion for Infrared and Visible Images
In this paper, a novel Bayesian fusion model is established for infrared and visible images.
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
DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion
Infrared and visible image fusion, a hot topic in the field of image processing, aims at obtaining fused images keeping the advantages of source images.
Ranked #5 on
Semantic Segmentation
on FMB Dataset
MFFW: A new dataset for multi-focus image fusion
It is found that current methods are evaluated on simulated image sets or Lytro dataset.
