Meta-learningSuper-resolutionResidual compensationExisting infrared and visible image fusion algorithms usually only input images of the same resolution and the obtained fusion image is still of low quality when the source image is of low resolution, which hinders further image analysis. In response to...
39 Deep Learning with Differential Privacy-Li Zhang 59:39 国际基础科学大会-Swarm of Micro Flying Robots in the Wild-Xin Zhou 54:00 国际基础科学大会-Lensless Imaging: Overview, Opportunities, and Challenges 59:57 国际基础科学大会-Heterogeneous Graph Neural Network-Chuxu Zhang 49:44 国际基础科学...
meta learning super-resolution 摘要 Infrared and visible image fusion has gained ever-increasing attention in recent years due to its great significance in a variety of vision-based applications. However, existing fusion methods suffer from some limitations in terms of the spatial resolutions of both...
Novel view synthesis has recently been revolutionized by learning neural radiance fields directly from sparse observations. However, rendering images with this new paradigm is slow due to the fact that an accurate quadrature of the volume rendering equation requires a large number of samples for each...
1. 问题介绍 元学习（Meta learning）是今年来的研究热点。简单来说，元学习可以理解为"学会学习"，...
Meta-learning可以理解为是一个工具，它可以应用于很多不同的场景中，利用meta-learning两层优化目标的...
又名《On First-Order Meta-Learning Algorithms》 openAI 2018 openai.com/blog/reptile arxiv.org/pdf/1803.02…阅读全文 赞同14 2 条评论 分享收藏 《TCML /SNAIL》 it.arxiv.org/pdf/1707.0 更名为:《A SIMPLE NEURAL ATTENTIVE META-LEARNER》 ICLR 2018 MAML 2017 abstract 深度神...
A versatile and effective approach to meta-learning is to infer a gradient-based up-date rule directly from data that promotes rapid learning of new tasks from the same distribution. Current methods rely on backpropagating through the learning process, limiting their scope to few-shot learning. ...
如果对这些原理还不熟悉的同学,建议先阅读Meta-learning核心思想及近年顶会3个优化方向一文。 1. 在迁移学习场景中的应用 在迁移学习中,Pretrain-Finetune是一种常用的方式。这种方式的问题在于,经常需要尝试不同的迁移策略来达到最优效果。例如,某一层的参数是迁移还是随机初始化;当pretrain阶段模型和finetune阶段...
介绍GraphSAGE模型之前需要先了解下归纳学习Inductive learning和转换学习Transductive learning。因为图结构数据和其他图像、文本数据不同,图结构数据中的每一个节点可以通过边的关系利用其他节点的信息,这就导致GCN模型需要输入整个图,所以会使用训练集、验证集和测试集,这个过程称为转换学习。这种转换学习存在一个明显的...