# Local Contrast Learning for One-Shot Learning
## 4 Experiments and Results
### Classification Evaluation
#### 4.1.1 Omniglot  
The Omniglot dataset consists of images across 1623 classes with only 20 images per class, from 50 different alphabets in total. The dataset is divided into a background (training or support) set of 30 alphabets with 964 characters and an evaluation (test) set of 20 alphabets with 659 characters. According to the suggestion from [PERSON] et al. [46], only the background set should be used for training, and one-shot learning results should be reported using alphabets from the evaluation set. Except for the training set of 30 alphabets with 964 characters, [PERSON] et al. [46] have provided smaller background sets, \"background small 1\" and \"background small 2\". Each of these contains just five alphabets and, respectively, contains 136 characters and 156 characters. Furthermore, to see how the models performed with more limited training classes, we built a tiny subset by picking up the first and second characters from each alphabet of the Omniglot background set, which was called \"background tiny\". The subset has 60 characters and 1200 samples. There are a few variant tests proposed by [PERSON], [PERSON] et al. [68] and [PERSON], [PERSON] et al. [69], which have different splits for the background set and evaluation set and different test trails from the BPL test protocol [46]. [PERSON] randomly picked up 1200 characters from Omniglot's background set and evaluation set as the training set and 423 characters as a test set. In [PERSON]'s split, the training set and test set have identical alphabets but have different characters. However, in the BPL test protocol, the training set and test set have completely different alphabets, that is to say, a model trained on some alphabets was used to classify some other alphabets. It is more challenging to recognize the images in the novel alphabet than in novel characters in identical alphabets. Some research has also followed [PERSON]'s, such as such as Matching Nets [68], MAML [70], MetaNet [71], CNPs [72], TAML [73], Cross-way training on Prototypical Networks [74], EDANet [75], and MSRN [76]. All test trials were only constructed from the test set and each trial consisted of a few characters picked from the test set. Only one image of each character was picked as a contrastive object, and one or a few images were picked as recognized objects. MetaNet [71] was trained and tested on BPL's split of 30 training alphabets with 964 classes; however, it formed 400 trials from the evaluation classes to test the model, so it cannot completely match BPL's result, which is evaluated on standard 400 trials. In order to extensively evaluate the performances of the LCNN and try to best match other approaches, in this section, we trained and tested the LCCN separately on the test protocol provided by [PERSON], [PERSON] et al. [68] with 1200 characters and on the test protocol provided by [PERSON] [46] with 964 characters. Furthermore, in order to show the advantages of LCNN on small data, we trained the LCNN on the first 60 characters and the first 156 characters of the 1200 training characters. We also trained the LCNN on \"background tiny\" and \"background small2\", but formed trials from the evaluation set rather than the standard 400 trials provided by [PERSON]. In all experiments in this section, the test trials were created from the test set using Algorithm 1, and the number of test trials \(n_{trial}\) was computed by Equation (15). \[n_{trial}=\left(EC\times KE\right)/(L+n_{shot}) \tag{15}\]  
Here, \(EC\) is the number of characters in the test set. \(KE\) is the number of samples per character. \(L\) is the number of contrastive objects in an LCC, and \(n_{shot}\) is the number of shots.