# Effect of Probabilistic Similarity Measure on Metric-Based Few-Shot Classification
## 5 Experimental Results
### GTSRB Traffic Sign Recognition  
As the third dataset, we chose a more practical one that is likely to be observed in real applications. GTSRB dataset [34] consists of various types of sign images as shown in Figure 8. There are 43 types of signs and total 51,839 images, which are color images taken from various angles and lighting conditions with various resolutions. We resize all the images to \(84\times 84\) pixel size. A total of 43 classes are divided into 22 and 21 classes used for training and testing, respectively. In order to maximize the generality of the embedding network through learning, data augmentation for the training set was performed according to the previous work [13]. Similar to the case of Omniglot, the number of training classes was increased by rotating the training images. Since it is a color image, the raw data format becomes \(84\times 84\times 3\) and is converted into a 1600-dimensional feature vector through the embedding network. In the learning phase, the embedding network is trained through episodes in the form of 20-way 5-shot. In the test phase, we start at 5-way 5-shot classification, and increase the ways by 5, finally reaching up to 21-way. Figure 9 shows the change of accuracy according to the number of classes for the proposed method and prototypical network. In these practical data, the effect of probabilistic similarity is observed more clearly. Especially, the superiority of the proposed method becomes clearer as the number of ways increases. The results are consistent with the assumptions about the correlation between the number of ways and the accuracy. In order to verify the efficiency of the proposed method compared with state-of-the-art methods, we also conducted experiments for 1-shot classification according to [34]. Since the proposed  
Figure 8: GTSRB image sample used in the experiment. Figure 7: Change of Classification accuracy on Multi-PIE depending on the number of ways. method cannot obtain difference vectors set \(\Omega_{\text{lst}}\) with a single support sample, episodic training was carried out with five support samples during the learning phase, and the covariance obtained from the train dataset was used for testing. From Table 2, we can see that the performance of the proposed method was higher than most conventional models except the VPE model with data augmentation, which is well-designed for the specific GTSRB data. To summarize the results of the three experiments, when the size of the way is low, the difference in performance from the existing models does not appear much. However, from Figures 4, 7 and 9, we can see a noticeable difference from the original model for the task with a larger number of ways, which has not been investigated in previous works. The larger the way, the more samples can be used to estimate the environmental distribution represented as the covariance matrix. Thanks to this advantage of the proposed method based on intra-class statistics, the performance degradation according to the increase in ways is gentler than that of the conventional model. Although the classification task for a large number of classes has great practical importance, it has rarely been dealt with in conventional works on few-shot classification. This paper has significance in that it presents a method to solve the many-class few-shot classification problem. \begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**21-Way 1-Shot Acc. (\%)**} \\ \cline{2-3}  & **Original Training Data** & **Train with Data Augmentation** \\ \hline ProtoNet [9] & 67.10 ** & 74.58 ** \\ QuadNet [20] & 45.20 * & - \\ SiamNet [6] & 22.45 * & 33.62 * \\ MatchingNet [8] & 26.03 * & 53.30 * \\ VAE [31] & 20.67 * & 22.24 * \\ VPE [15] & 56.98 * & 81.27 * \\
**Proposed** & **69.62** & **75.56** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The 1-shot classification accuracies on GTSRB.