这篇文章提出的npair loss整体上非常的简单但是也make sense,dml里很多loss function基本上是 negative sample mining 和 loss 的计算包一起然后被称之为新的loss function,实际上底层用的loss 计算还是那些基本的东西。比如说这篇(当然这篇工作还是非常有价值的,至少在实践中是work的,不像一些奇奇怪怪的paper,思路...
与常见的三元组损失(triplet loss)中一个锚样本、一个正样本和一个负样本不一样,论文提出了一个(N+1)元组的损失,来使一个正样本与N−1个负样本区分开来。 Deep Metric Learning with Multiple Negative Examples 在三元组损失中,如果要使得损失尽可能低,显然有这么几种情况: 缩短正样本与锚样本的距离; 增大...
Deep metric learning has gained much popularity in recent years, following the success of deep learning. However, existing frameworks of deep metric learning based on contrastive loss and triplet loss often suffer from slow convergence, partially because they employ only one negative example while not...
A deep-learning-enabled, high-throughput multiscale MSI framework MEISTER integrates high-throughput MS experiments, a deep-learning-based signal reconstruction method and data-driven high-dimensional MSI analysis to enable brain-wide, multiscale profiling of brain biochemistry. To resolve detailed chemica...
github@malongtech.com Citation If you use this method or this code in your research, please cite as: @inproceedings{wang2019multi, title={Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning}, author={Wang, Xun and Han, Xintong and Huang, Weilin and Dong, Dengke and ...
Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning(PDF) Author:Xun Wang, Xintong Han, Weilin Huang, Dengke Dong, Matthew R. Scott CVPR2019 (Citations:237) 核心思想: 根据本文一作在知乎上的回答,本文的一大亮点是提出了通用对加权框架,基于该框架可以对市面上常见的Loss进行分析...
scPoli is a semi-supervised generative deep learning method comprising two components, an unsupervised backbone based on CVAEs27 and a cell type supervised component leveraging prototype networks28. In the following we first outline the data generation process describing different inputs for the model...
2.2. Deep Learning-Based Pansharpening Deep learning-based methods have recently shown great potential for pansharpening thanks to their powerful nonlinear mapping capability. A comprehensive review about the topic with a critical comparison of widely-used approaches together with a freely distributed tool...
Owing to the popularity of deep learning (DL) and the advantage of its end-to-end characteristics, DL has been widely applied in computer vision [5,6], among which feature-learning-based semantic segmentation methods for both BE and CD tasks have been widely studied. Building feature represent...
In the field of multi-agent deep reinforcement learning (MADRL), agents can improve the overall learning performance and achieve their objectives by communication. Agents can communicate various types of messages, either to all agents or to specific agent groups, or conditioned on specific ...