Search Results for author:
Found 7 papers, 3 papers with code
FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning
Inspired by the abundance of publicly available remote sensing projects and the burgeoning development of deep learning in computer vision, our research focuses on assessing fire risk using remote sensing imagery.
Ranked #1 on
Remote Sensing Image Classification
on FireRisk
DINO-MC: Self-supervised Contrastive Learning for Remote Sensing Imagery with Multi-sized Local Crops
Due to the costly nature of remote sensing image labeling and the large volume of available unlabeled imagery, self-supervised methods that can learn feature representations without manual annotation have received great attention.
Ranked #1 on
Multi-Label Image Classification
on BigEarthNet-10%
(using extra training data)
Reproducibility and Baseline Reporting for Dynamic Multi-objective Benchmark Problems
Our framework is based on an extension of PlatEMO, allowing for the reproduction of results and performance measurements across a range of dynamic settings and problems.
An Instance Space Analysis of Constrained Multi-Objective Optimization Problems
Specifically, we scrutinise the multi-objective landscape and introduce new features to describe the multi-objective-violation landscape, formed by the interaction between constraint violation and multi-objective fitness.
Machine learning with incomplete datasets using multi-objective optimization models
We investigate the reliability and robustness of the proposed model using experiments by defining several scenarios in dealing with missing values and classification.
Boosting Ant Colony Optimization via Solution Prediction and Machine Learning
In the first phase of our ML-ACO algorithm, an ML model is trained using a set of small problem instances where the optimal solution is known.
Dynamic Multi-objective Optimization of the Travelling Thief Problem
A combined approach that mixes solution generation methods to provide a composite population in response to dynamic changes provides improved performance in some instances for the different dynamic TTP formulations.
