基于参数隔离的方法:为每个新任务添加额外参数到动态架构中。 多域增量学习 (Multi-Domain Incremental Learning) 多域增量学习中关于分类任务的工作包括:渐进神经网络、动态可扩展网络(DENs)、将控制模块连接到基础网络的方式。 最近的工作基于参数隔离技术将特定领域参数子集用于每个任务,但主要侧重于分类问题。与本任务...
The prominence of deep learning, large amount of annotated data and increasingly powerful hardware made it possible to reach remarkable performance for supervised classification tasks, in many cases saturating the training sets. However the resulting models are specialized to a single very specific task...
图3. The pipeline of Incremental Learning Through Deep Adaption 图4. Controller Module 2. 再看Cross-sensor Cross-sensor本身,既可以是训练集的cross出发,比如我如何设计一个model尽可能的使用多(不同sensor)的数据集,就像本文把这个描述为multi-domain问题——当然这样就增加了数据,也就提升了效果;又可以从结...
Top Abstract Inside the e-learning platforms, it is important to manage the user competencies profile and to recommend to each user the most suitable documents and persons, according to his or her acquired knowledge, to their long-term i... M Brut,F Sedes,C Zayani - IGI Global 被引量: ...
It is an incremental learning algorithm based on the random vector function link neural network (RVFLNN). For this method, firstly, the original data are projected in the feature space using a linear function and transformed into features notes of the BLS. Then enhancement notes are generated by...
Using this method in an incremental search, it is possible to find the most important features. 4.2 Multi-task learning Multi-task learning is a statistical spatial feature mapping framework where identical input data from different tasks are jointly learned. Joint learning of unrelated tasks can ...
ECCV-22Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation Prototype continual domain adaptation 基于原型的类增量domain adaptation Federated Semi-Supervised Domain Adaptation via Knowledge Transfer Federated semi-supervised DA 联邦半监督DA ...
Deep Learning on domain adaptation, transfer and multi-task applications Incremental, online and active transfer for open-ended learning Innovative adaptive procedures with applications e.g. in computer vision or computational biology Domain adaptation theory ...
With the flourishing development of deep learning, synthetic aperture radar (SAR) ship detection based on this method has been widely applied across various domains. However, most deep-learning-based detection methods currently only use the amplitude information from SAR images. In fact, phase informa...
short-term wind power prediction; multi-domain feature fusion; wavelet transform; similarity; deep learning; wind turbines1. Introduction The world is facing challenges such as climate change, resource shortages, and environmental pollution. Gradually replacing traditional fossil fuel power generation with...