Search Results for author:
Found 6 papers, 1 papers with code
RKFNet: A Novel Neural Network Aided Robust Kalman Filter
Driven by the filtering challenges in linear systems disturbed by non-Gaussian heavy-tailed noise, the robust Kalman filters (RKFs) leveraging diverse heavy-tailed distributions have been introduced.
Knowledge Distillation for Road Detection based on cross-model Semi-Supervised Learning
The proposed semi-supervised learning-based knowledge distillation (SSLKD) approach demonstrates a notable improvement in the performance of the student model, in the application of road segmentation, surpassing the effectiveness of traditional semi-supervised learning methods.
DiverseNet: Decision Diversified Semi-supervised Semantic Segmentation Networks for Remote Sensing Imagery
There is still a lack of lightweight and efficient perturbation methods to promote the diversity of features and the precision of pseudo labels during training.
Confidence-Guided Semi-supervised Learning in Land Cover Classification
Especially in the application of land cover classification, pixel-level manual labelling in large-scale imagery is labour-intensive, time-consuming and expensive.
High-precision Density Mapping of Marine Debris and Floating Plastics via Satellite Imagery
As such, this paper comprised of three main components: (1) the development of a machine learning model, (2) the construction of the MAP-Mapper, an automated tool for mapping marine-plastic density, and finally (3) an evaluation of the whole system for out-of-distribution test locations.
Convergence Guarantees for Non-Convex Optimisation with Cauchy-Based Penalties
In this paper, we propose a proximal splitting methodology with a non-convex penalty function based on the heavy-tailed Cauchy distribution.
