# Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings
## 4 Experiments
### Utilization of Model Uncertainty during Inference
#### 4.3.2 Relationship between the Feature Vector and Estimated Model Uncertainty  
In order to explore the potential of incorporating estimated uncertainties during inference in order to improve the feature vectors, we investigated the relationship between the estimated uncertainty vectors and the feature vector. With regard to the model uncertainty estimate, we observed a correlation between the uncertainty values and the absolute values of the feature vector.  
Figure 5(a,c) visualizes exemplary feature vectors and the associated model uncertainty values. Figure 5(b,d) shows the correlation.  
In Figure 5(a,c) an excerpt of feature vectors for three input images is presented. The dots represent the values of the normalized feature vector \(\frac{\mu}{\|\mu\|_{2}}\), which is the basis for calculating the cosine distance. The error bars illustrate the estimated model uncertainty values as standard deviation.  
In case of low estimated model uncertainty values for all three feature vectors, it can be observed that at specific positions, such as those highlighted as A, B, and F, we can see that the feature vectors exhibit relatively low differences for a matching individual (a)  
Figure 6: Excerpt of the normalized feature vector (elements 1400–1450, \(d=2048\)) for (**a**) two images of the same identity and (**c**) two images of different identities. The corresponding model uncertainty is shown as error bars representing the standard deviation. The highlighted positions marked as A–G are analyzed in the text. The correlation of the absolute values of the feature vector and the model uncertainty is shown for two input images in (**b,d**). The images are part of the Market-1501 dataset [68].  
and higher differences for different individuals (b). These differences are then aggregated, resulting in a low distance between the feature vectors of matching individuals and a higher distance between the feature vectors of different individuals.  
However, the values of the feature vector at the highlighted positions E and G attain high absolute values for the images framed in blue and green of matching identity. Concurrently, the estimated model uncertainty is also relatively large at these locations. It is evident that there are large differences between the feature vectors of the two images depicting the same individual (a) at the highlighted positions. If the estimated model uncertainty is not taken into account, the differences would result in a greater distance between the feature vectors. Hence, the images would be evaluated as depicting individuals with notable dissimilarities.  
For the feature vector corresponding to the non-matching image framed in red, we can also observe larger absolute values at the highlighted positions C and D. However, the estimated model uncertainty is much less pronounced than in the case described above.  
Therefore, we hypothesize that the estimated model uncertainty is a valuable source of information to judge whether some outliers can be trusted or whether they are the result of uncertain model outputs.  
Figure 6b,d illustrate the correlation between the elements of the estimated model uncertainty vector and the absolute values of the feature vector for two exemplary input images. Once more, the outliers can be primarily attributed to large model uncertainty. For the majority of feature vector elements, the estimated model uncertainty is relatively low.