# Effect of Probabilistic Similarity Measure on Metric-Based Few-Shot Classification
## 6 Conclusions  
For the conventional metric-based few-shot classification methods, the main focus is to find a good metric space on which the intra-class variations of unseen classes are minimized. Under the premise that this can be successfully achieved by using a deep embedding network and episodic learning strategy, the classification is performed by a simple distance-based classifier using standard distance such as cosine and Euclidean. In this paper, we suggest a way of improving the distance-based classifier by using a probabilistic similarity, which is derived from a class-independent environmental factor estimated by using intra-class difference vectors. Taking the intra-class difference vector, we can exclude the class-specific components that are hard to estimate with a limited number of samples per class.  
Although the probabilistic similarity based on intra-class statistics has already been used in classical pattern recognition studies, the conventional works suggest a premise that a good feature representation for the input data is provided in advance. In the proposed method, however, the feature extraction module (embedding network) is also trained by using loss signals from the classifier with probability similarity. Essentially, the probabilistic similarity assumes that all the classes in a domain have the same intra-class variations, and this is consistent with the prototypical network model, which assumes that it is possible to find a good embedding space where each class has a single prototype and its variations are very limited. Owing to this consistency, the proposed method achieves improved performance in the experiments. However, it is noteworthy that the proposed algorithm has no constraint on the embedding network model and better performance can be expected by using a more complex embedding network. Finally, since the good performance for problems with many classes and the simplicity of implementation can be practical strengths of the proposed method, its practical applications in various fields can also be an interesting follow-up.  
Conceptualization, H.P.; data curation, Y.L. and H.P.; formal analysis, Y.L. and H.P.; methodology, Y.L. and H.P.; software, Y.L.; validation, Y.L. and H.P.; visualization, Y.L.; writing--original draft, Y.L. and H.P.; writing--review and editing, Y.L. and H.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
This work was supported by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20204010600060). This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2021-0-02068, Artificial Intelligence Innovation Hub).  
The authors declare no conflict of interest.