【ECCV 2020】UDA with Noise Resistible Mutual-Training for Person Re-identification （NRMT）

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1. 背景知识

Pseudo-label based self-training

Pseudo-label based self-training is one of the representative techniques to address UDA.

Problem #1

How- ever, label noise caused by unsupervised clustering is always a trouble to self-training methods. //然而，无监督聚类引起的标签噪声一直是自训练方法的一大难题。

2. 内容概要

本文工作

• We present a novel noise resistible mutual-training method for unsupervised domain adaptation in person re-ID, which exploits dual network interaction to depress noises in pseudo- labels of unsupervised iterative training on the target data. //提出了一种新的无监督域自适应的抗噪声互训练方法，利用双网络交互抑制目标数据无监督迭代训练中伪标签中的噪声。 解决Problem #1
• We introduce a collaborative clustering to ease the fitting to noisy instances by the memoriza- tion effects of deep networks. //我们引入了一种协作聚类方法，通过深度网络的记忆效应来简化对噪声实例的拟合。
• We propose a mutual instance selection based on the peer-confidence and relationship disagreement of networks on triplets of instances to select reliable and informative instances in a mini-batch. //提出了一种基于网络对三组实例的信任和关系不一致的互实例选择方法，在小批量中选择可靠且信息丰富的实例。

实验效果

the proposed method outperforms the state-of-the-art UDA methods for person re-ID.

相关工作

• Unsupervised Domain Adaptation.
• UDA for Person re-ID.
• Deep Learning with Noisy Labels.

数据集

• Market-1501 [52],
• ukeMTMC-reID [26,54]
• MSMT17 [39]

3. 方法提要

Noise Resistible Mutual-Training (NRMT) method

To depress noises in pseudo-labels, this paper proposes a Noise Resistible Mutual-Training (NRMT) method, which maintains two networks during training to perform collaborative clus- tering and mutual instance selection //为了抑制伪标签中的噪声，本文提出了一种抗噪声相互训练(NRMT)方法，该方法在训练过程中保持两个网络，以进行协作聚类和相互实例选择

• collaborative clus- tering eases the fitting to noisy instances by allowing the two networks to use pseudo-labels provided by each other as an additional supervi- sion. //协作聚类允许两个网络使用彼此提供的伪标签作为额外的监督，从而简化了对噪声实例的拟合。
• mutual instance selection further selects reliable and informative instances for training according to the peer-confidence and relationship disagreement of the networks. //互实例选择进一步根据网络的同伴信任和关系分歧，选择可靠且信息丰富的实例进行训练。

方法框架

算法描述

实验结果

4. 方法详解

。。。

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