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Found 13 papers, 2 papers with code
SSDiff: Spatial-spectral Integrated Diffusion Model for Remote Sensing Pansharpening
Pansharpening is a significant image fusion technique that merges the spatial content and spectral characteristics of remote sensing images to generate high-resolution multispectral images.
A Novel State Space Model with Local Enhancement and State Sharing for Image Fusion
However, due to the nature of images different from casual language sequences, the limited state capacity of Mamba weakens its ability to model image information.
Point cloud-based registration and image fusion between cardiac SPECT MPI and CTA
Fourthly, we employed ICP, SICP or CPD algorithm to achieve a fine registration for the point clouds (together with the special points of APIGs) of the LV epicardial surfaces (LVERs) in SPECT and CTA images.
Cross-tokamak Disruption Prediction based on Physics-Guided Feature Extraction and domain adaptation
The second step is to align a few data from the future tokamak (target domain) and a large amount of data from existing tokamak (source domain) based on a domain adaptation algorithm called CORrelation ALignment (CORAL).
An Empirical Study on the Holiday Effect of China's Time-Honored Companies
The stock segment of China's time-honored brand enterprises has an important position in our securities stock market.
Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection
Finally, we optimize precursor labeling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
Multi-Scaling Differential Contraction Integral Method for Inverse Scattering Problems with Inhomogeneous Media
Practical applications of microwave imaging often require the solution of inverse scattering problems with inhomogeneous backgrounds.
IDP-PGFE: An Interpretable Disruption Predictor based on Physics-Guided Feature Extraction
Understanding why a predictor makes a certain prediction can be as crucial as the prediction's accuracy for future tokamak disruption predictors.
Exposing Semantic Segmentation Failures via Maximum Discrepancy Competition
A natural question then arises: Does the superior performance on the closed (and frequently re-used) test sets transfer to the open visual world with unconstrained variations?
Social Biases in NLP Models as Barriers for Persons with Disabilities
Building equitable and inclusive NLP technologies demands consideration of whether and how social attitudes are represented in ML models.
Noisier2Noise: Learning to Denoise from Unpaired Noisy Data
We present a method for training a neural network to perform image denoising without access to clean training examples or access to paired noisy training examples.
Causally Driven Incremental Multi Touch Attribution Using a Recurrent Neural Network
The specific formulation of the Shapley Value we implement respects incrementality by allocating the overall incremental improvement in conversion to the exposed ads, while handling the sequence-dependence of exposures on the observed outcomes.
Enlightening Deep Neural Networks with Knowledge of Confounding Factors
Deep learning techniques have demonstrated significant capacity in modeling some of the most challenging real world problems of high complexity.
