方法概述

1，提出一种创新的带有混合内存的自我进度（self-paced）对比学习框架。其中，混合内存动态生成源域类级、目标域聚类级和无聚类实体集的监督信号。
2，self-paced 方法可以生成更加可信的聚类来精炼混合内存和学习目标。

文章目录

方法概述
内容概要
- 工作概述
- 成果概述
方法详解
- 方法框架
- 算法描述
- 具体实现
实验结果
总体评价
引用格式
参考文献

内容概要

论文名称	简称	会议/期刊	出版年份	baseline	backbone	数据集
Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID	SpCL	NIPS	2020	【MMT】Ge, Y., Chen, D., Li, H.: Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification. In: International Conference on Learning Representations. pp. 1–15 (2020)	ImageNet-pretrained [7] ResNet-50 [18] ，use DBSCAN [9] for clustering	Market-1501\MSMT17

在线链接：https://proceedings.neurips.cc/paper/2020/file/821fa74b50ba3f7cba1e6c53e8fa6845-Paper.pdf
源码链接： https://github.com/yxgeee/SpCL.

工作概述

1。we propose a novel self-paced contrastive learning framework with hybrid memory. The hybrid memory dynamically gen- erates source-domain class-level, target-domain cluster-level and un-clustered instance-level supervisory signals for learning feature representations. Different from the conventional contrastive learning strategy, the proposed framework jointly distinguishes source-domain classes, and target-domain clusters and un-clustered instances.

the proposed self-paced method gradually creates more reliable clusters to refine the hybrid memory and learning targets, and is shown to be the key to our outstanding performance.

成果概述

Our method outperforms state-of- the-arts on multiple domain adaptation tasks of object re-ID and even boosts the performance on the source domain without any extra annotations. Our general- ized version on unsupervised object re-ID surpasses state-of-the-art algorithms by considerable 16.7% and 7.9% on Market-1501 and MSMT17 benchmarks†

方法详解

方法框架

Figure 2: (a) The illustration of the proposed unified framework with a novel hybrid memory. (b) The proposed reliability criterion for measuring the cluster independence‡ and compactness

算法描述

具体实现

1，用于模型训练的数据分为三个部分。源域带标注样本、目标域聚类伪标签样本，目标域无聚类实体样本（一个样本视为一个单独的类），整体的损失函数为公式1。

2，混合内存中存储了两个部分的数据。一是源域类中心特征{w}，二是目标域所有的样本特征{v}。

3，对{v}进行聚类，可以得到目标域聚类伪标签和无聚类实体样本。对聚类的伪标签样本求聚类中心，得到{c}（公式2），用于公式1的计算。

4，内存更新。w 和v 都是根据原有的版本和新计算的版本进行加权求和，分别如公式3和公式4所示。

5，Self-paced learning，决定如何划分目标域聚类伪标签样本和无聚类实体样本。设计了聚类独立性和紧凑性两个指标。独立性计算如公式5所示，紧凑性计算如公式6所示，最后通过两个指标来决定是否为聚类样本。

we preserve independent clusters with compact data points whose Rindep > α and Rcomp > β

实验结果

总体评价

1，将数据分为三个部分这一点还是很具有创新性的，如图1所示，确实有更好的利用有限数据。

2，利用混合内存进行存储其实和hpla很相似，都把上一阶段的计算结果进行了存储。不同的是，他只进行了加权求和，而hpla进行了知识融合。
3，独立性和紧凑性设计思路很常见，可优化。

引用格式

@inproceedings{DBLP:conf/nips/Ge0C0L20,
author = {Yixiao Ge and
Feng Zhu and
Dapeng Chen and
Rui Zhao and
Hongsheng Li},
title = {Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive
Object Re-ID},
booktitle = {NeurIPS},
year = {2020}
}

小样本学习与智能前沿（下方↓公众号）后台回复“SpCL，即可获得论文电子资源。

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