# Unsupervised Domain Adaptive Person Re-Identification via Intermediate Domains
###### Abstract  
Recent years have witnessed outstanding success in supervised domain adaptive person re-identification (ReID). However, the model often suffers serious performance drops when transferring to another domain in real-world applications. To address the domain gap situations, many unsupervised domain adaptive (UDA) methods have been proposed to adapt the model trained on the source domain to a target domain. Such methods are typically based on clustering algorithms to generate pseudo labels. Noisy labels, which often exist due to the instability of clustering algorithms, will substantially affect the performance of UDA methods. In this study, we focused on intermediate domains that can be regarded as a bridge that connects source and target domains. We added a domainness factor in the loss function of SPGAN that can decide the style of the image generated by the GAN model. We obtained a series of intermediate domains by changing the value of the domainness factor. Pseudo labels are more reliable because intermediate domains are closer to the source domain compared with the target domain. We then fine-tuned the model pre-trained with source data on these intermediate domains. The fine-tuning process was conducted repeatedly because intermediate domains are composed of more than one dataset. Finally, the model fine-tuned on intermediate domains was adapted to the target domain. The model can easily adapt to changes in image style as we gradually transfer the model to the target domain along the bridge consisting of several intermediate domains. To the best of our knowledge, we are the first to apply intermediate domains to UDA problems. We evaluated our method on Market1501, DukeMTMC-reID and MSMT17 datasets. Experimental results proved that our method brings a significant improvement and achieves a state-of-the-art performance. p 20  
Thus, unsupervised domain adaptation (UDA) methods are proposed to adapt the model trained on one domain to a new domain without identity labels. Recent UDA methods [5, 6] for ReID aim to learn models that appropriately fit on the target domain with clustering algorithms. Clustering-based methods first train a model on the source domain and then fine-tune the model based on pesudo labels. The process of model training and clustering is carried out iteratively until the model converges. Although pesudo labels generated through clustering algorithms can improve the performance of the model on the target domain to some extent, they often contain noisy (wrong) labels due to the gap between source and target domains that can mislead the training of the model. In this work, we focus on the intermediate domains between source and target domains. Many studies have proven the effectiveness of intermediate domains in domain adaptation tasks [7]. Images from intermediate domains contain both the style of the source and target domains. Intermediate domains will be utilized as a bridge to narrow the domain gap between source and target domains. Pseudo labels generated on intermediate domains are reliable given the small domain divergence between source and intermediate domains. Our work is based on DLOW [8] and SPGAN [9], which is one of the state-of-the-art (SOTA) image-to-image translation methods for ReID. We added a domainness factor in the loss function of SPGAN on the basis of DLOW to determine the style of output images. We fixed the domainness factor during the training of GAN to generate a specific image style. Then, the value of the domainness factor was set up manually to obtain a series of intermediate domains. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Rank-1** & **mAP** \\ \hline Market1501 & 94.5 & 91.0 \\ M\(\rightarrow\)D & 47.5 & 37.6 \\ DukeMTMC-reID & 86.7 & 84.1 \\ D\(\rightarrow\)M & 61.3 & 43.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The performance of different models evaluated on cross-domain datasets. M\(\rightarrow\)D means that we train the model on the Market1501 and evaluate it on the DukeMTMC-reID.