# Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings
## 4 Experiments
### Utilization of Model Uncertainty during Inference
#### 4.3.1 Comparison with the State of the Art  
In Table 1, we list re-identification results of approaches that employ uncertainty estimation. The training and evaluation dataset was Market-1501 [68]. The results were either reported in related work [22; 28; 52] or are the results of our experiments. It should be noted that the baseline (displayed in gray) in each of the three aforementioned publications differed. The results reported by [PERSON] et al. [22] are based on the DenseNet baseline AWTL [73]. The results reported by [PERSON] et al. [28] are based on a ResNet-50 baseline and the reported mean and standard deviation were calculated over 5 runs. The results reported by [PERSON] et al. [52] are based on the ResNet-50 baseline IDE [74]. It is therefore evident that attaining an improvement is considerably easier when one starts from a relatively weak baseline. We follow the attempt of [PERSON] et al. [25] and establish the strong BOT baseline [66] as a starting point. We report the average result and the standard deviation over 20 runs for all approaches with publicly available source code by the authors, namely Distribution Net (DistNet) [51], Probabilistic Face Embeddings (PFE) [21], and Uncertainty Aware Learning (UAL) [25]. For each approach, we list which types of uncertainty are estimated  
Figure 4: Correlation between the data, model, and distributional uncertainty estimates (**a–c**). Each point represents a pair of uncertainty estimates for a sample of the set of query and gallery images of the Market-1501 dataset [68]. Additionally, in (**d**), the contour lines of the distributions of the query and distractor sets are displayed. The distractor sets (D1)–(D4) are explained in Section 4.4.2. The colors are identical to those used in that section. during training and which types of uncertainties are utilized during inference for refining the feature vector. In our approach, we utilize the estimated model uncertainty \(\Sigma^{(M)}\) and the estimated data uncertainty \(\Sigma^{(D)}\) to refine the feature embedding vector \(\mu\). Therefore, we name our approach uncertainty-based embedding refinement (UBER). Furthermore, the incorporation of distributional uncertainty into the refinement of the feature embedding vector yields additional minor improvements (Section 4.3.4) and can be used for identifying input images that are out-of-distribution (Section 4.4). Additionally to the mean and standard deviation reported in Table 1, the results of all 20 runs of our experiments are shown in Figure 5 as box plots. We can observe, that the performance of DistNet [51] declines significantly when it is applied to a strong baseline. This is consistent with the reported result by [PERSON] et al. [28] (see Table 1). PFE [21] does not modify the feature vector during training. We observed that the learned uncertainty vector had very small values that barely affected the performance across various attempts to use them for embedding refinement. Therefore the result of PFE is basically identical to the baseline. UAL [25], which is the basis for our architecture, estimated uncertainties that helped to improve the performance over the strong baseline. The reported performance by [PERSON] et al. [25] (mAP = 87.0, rank-1 = 95.2) could be reproduced in our experiments, although it is probably a cherry-picked result. Our approach UBER utilizes model uncertainty and data uncertainty. This helped to further improve the performance noticeably over the UAL result. 80% of our models trained in the