# Local Contrast Learning for One-Shot Learning
## 4 Experiments and Results
### Small Data Deep Learning  
In order to demonstrate the performances on a small training dataset, we designed a series of experiments on Omniglot [46] and CASIA-HWDB1.1 [66]. We constructed the different experiments by adjusting the parameters of training datasets, including the number of classes and the number of samples per class, the number of training steps, the number of network layers, and the number of shots. In all experiments, if not specified, the number of the network layers was 26 and the number of parameters was about 488 k, \(d_{1}\) and \(d_{2}\) were separately set to 9600 and 12,800 m was 16,000, and the image size was 64 x 64. In the Omniglot experiments, the BPL test protocol was used for one-shot classification and the Variant test protocol for five-shot classification and fifteen-shot classification. The selection of training data followed [PERSON]'s [46] suggestion. We also constructed two tiny training datasets based on Omniglot background small 1. One was based on the Latin alphabet with 26 characters and another was based on the Korean alphabet with 40 characters. All experiments used the standard BPL test protocol. In the CASIA-HWDB1.1 experiments, the training characters were selected from the first 3155 characters; for example, the training data of the experiment \"h1.1\" in Table 3 selected the first 500 characters and drawn by the first 5 calligraphers. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Chars** & **1-Shot** & **5-Shot** \\ \hline Matching Nets & & & \\ HWDB60 & 60 & 69.78 & - \\ HWDB200 & 200 & 81.67 & - \\ HWDB3155 & 3155 & 85.83 & - \\ \hline LCNN & & & \\ HWDB60 & 60 & 89.85 \(\pm\) 0.26 & 97.70 \(\pm\) 0.13 \\ HWDB200 & 200 & 96.33 \(\pm\) 0.13 & 99.41 \(\pm\) 0.06 \\ HWDB3155 & 3155 & 97.32 \(\pm\) 0.16 & 99.60 \(\pm\) 0.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Twenty-way classification accuracy (%) on HWDB. Results are accuracies averaged over 100-time runs and with 95% confidence intervals where reported. The models labeled by HWDB60, HWDB200, and HWDB3155 were, respectively, trained on the first 60, 200, and 3155 characters of HWDB with 20 samples per character. ’-’ means no result. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**ID** & **Training Dataset** & **Classes** & **Samples** & **Shots** & \begin{tabular}{l} **Average** \\ **Accuracy** \\ \end{tabular} & \begin{tabular}{l} **Maximum** \\ **Accuracy** \\ \end{tabular} & \
\begin{tabular}{l} **Minimum** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{l} **Variance** \\ **Accuracy** \\ \end{tabular} & **Amount** \\ \hline o1.1 & \(\text{ommiglot\_Latin}\) & 26 & 20 & 1 & 66.86 & 67.50 & 66.00 & 0.15 & 520 \\ o1.2 & \(\text{ommiglot\_Latin}\) & 26 & 20 & 5 & 85.14 & 88.20 & 82.20 & 2.04 & 520 \\ o1.3 & \(\text{ommiglot\_Latin}\) & 26 & 20 & 15 & 89.49 & 91.94 & 85.83 & 3.23 & 520 \\ o1.4 * & \(\text{ommiglot\_small2}\) & 156 & 5 & 1 & 90.51 & 91.00 & 90.00 & 0.08 & 780 \\ o1.5 * & \(\text{ommiglot\_small2}\) & 156 & 5 & 5 & 97.75 & 99.04 & 96.15 & 0.59 & 780 \\
**01.6** + & \(\text{ommiglot\_tiny}\) & **60** & **20** & **1** & **97.99** & **98.25** & **97.25** & **0.06** & **1200** \\ o1.7** + & \(\text{ommiglot\_small1}\) & 136 & 20 & 1 & 98.65 & 99.50 & 98.00 & 0.06 & 2720 \\
**o1.8** + & \(\text{ommiglot\_small2}\) & **156** & **20** & **1** & **99.17** & **99.50** & **98.75** & **0.04** & **3120** \\ o1.9** + & \(\text{ommiglot}\) & 964 & 20 & 1 & 98.92 & 99.50 & 98.50 & 0.04 & 19,280 \\ \hline h1.1 - & HWDB & 500 & 5 & 1 & 79.42 & 82.00 & 76.36 & 2.63 & 2500 \\ h1.2 - & HWDB & 200 & 20 & 1 & 95.02 & 96.18 & 93.82 & 0.40 & 4000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of small data experiments on Omniglot and HWDB with 20-way accuracy (%). ID with ’ means that the network has 86 layers, and ID with ’ means that the network has 46 layers; otherwise, the network has 26 layers. ID with + means that the image sizes are 28 × 28 and the images are augmented by 90, 180, and 270 degrees rotated and resized.