Optimizing Kernel-Target Alignment for cloud detection in multispectral satellite images
The optimization of Kernel-Target Alignment (TA) has been recently proposed as a way to reduce the number of hardware resources in quantum classifiers. It allows to exchange highly expressive and costly circuits to moderate size, task oriented ones. In this work we propose a simple toy model to study the optimization landscape of the Kernel-Target Alignment. We find that for underparameterized circuits the optimization landscape possess either many local extrema or becomes flat with narrow global extremum. We find the dependence of the width of the global extremum peak on the amount of data introduced to the model. The experimental study was performed using multispectral satellite data, and we targeted the cloud detection task, being one of the most fundamental and important image analysis tasks in remote sensing.
Code (0)
등록된 구현이 없습니다.
Tasks
Cloud DetectionSimilar Papers 제목 키워드 기반
Cloud Detection in Multispectral Satellite Images Using Support Vector Machines With Quantum Kernels
Support vector machines (SVMs) are a well-established classifier effectively deployed in an array of pattern recognition and classification tasks. In this work, we consider extending classic SVMs with quantum kernels and…
Cloud DetectionMatching biomolecular structures by registration of point clouds
Motivation: Assessing the match between two biomolecular structures is at the heart of structural analyses such as superposition, alignment and docking. These tasks are typically solved with specialized structure-matchin…
global-optimizationPoint Cloud Completion by Learning Shape Priors
In view of the difficulty in reconstructing object details in point cloud completion, we propose a shape prior learning method for object completion. The shape priors include geometric information in both complete and th…
Generative Adversarial NetworkPoint Cloud CompletionDetecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machines
Support vector machines (SVMs) are a well-established classifier effectively deployed in an array of classification tasks. In this work, we consider extending classical SVMs with quantum kernels and applying them to sate…
Cloud DetectionKernel Alignment for Unsupervised Transfer Learning
The ability of a human being to extrapolate previously gained knowledge to other domains inspired a new family of methods in machine learning called transfer learning. Transfer learning is often based on the assumption t…
Transfer Learning