总结 本文从传统的度量学习一路讲到近些年提出的深度度量学习,是入门度量学习/对比学习的必读文章。 度量学习 图1 度量学习 度量学习是从数据中学习一种度量数据对象间距离的方法。如图1c所示,其目标是使得在学得的距离度量下,相似对象间的距离小,不相似对象间的距离大。 度量学习根据是否转换原始特征空间后再进行度...
度量学习是从数据中学习距离方法，以区分相似与不相似对象。其核心目标是让相似对象之间的距离小，不相似对象之间的距离大。度量学习可以分为基于原始特征空间的方法与基于投影矩阵的方法。原始特征空间方法，如KNN算法，直接基于欧氏距离计算对象相似性。投影矩阵方法，如马氏距离，通过投影矩阵转换特征空间后再...
A survey on metric learning for feature vectors and structured data (Figure 1) Deep metric learning using Triplet network (triplet loss) FaceNet: A Unified Embedding for Face Recognition and Clustering (semi-hard,L2-normalization) Deep Metric Learning via Lifted Structured Feature Embedding (Lifted ...
Deep Metric Learning: A Survey 喜欢 0 阅读量: 902 作者: M Kaya 摘要: Metric learning aims to measure the similarity among samples while using an optimal distance metric for learning tasks. Metric learning methods, which generally use a linear projection, are limited in solving real-world ...
Meta-learning is one approach to address this issue, by enabling the network to learn how to learn. The field of Deep Meta-Learning advances at great speed, but lacks a unified, in-depth overview of current techniques. With this work, we aim to bridge this gap. After providing the ...
《Deep Long-Tailed Learning: A Survey》 深度长尾学习: 调查 作者 Yifan Zhang、Bingyi Kang、Bryan Hooi、Shuicheng Yan(IEEE Fellow)和 Jiashi Feng 来自新加坡国立大学计算机学院、字节跳动 AI Lab 和 SEA AI Lab 初读 摘要 长尾类别不平衡(long-tailed class imbalance): ...
深度学习图像分割综述📖 Image Segmentation Using Deep Learning: A Survey 原文连接:https://arxiv.org/pdf/2001.05566.pdf Abstract 图像分割应用包括场景理解、医学图像分析、机器人感知、视频监控
论文阅读08——《Deep Learning on Graphs: A Survey》 神经网络深度学习survey论文模型 深度学习在许多领域都是成功的,从声学、图像到自然语言处理。然而,由于图的独特特性,将深度学习应用于无处不在的图数据并非易事。最近,大量的研究致力于将深度学习方法应用于图,从而在图分析技术方面取得了有益的进展。在这项...
, and FaceNet_Re) in terms of recognition accuracy and speed. The systems in comparison use three main deep network architectures discussed in this survey; ResNet, GoogleNet, and Inception-ResNet and the two main classification approaches, subject-specific modeling with SVM and template learning....
Metric learning aims to measure the similarity among samples while using an optimal distance metric for learning tasks. Metric learning methods, which generally use a linear projection, are limited in solving real-world problems demonstrating non-linear characteristics. Kernel approaches are utilized in ...