# Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images
## 5 Discussion  
In Section 4, it was occasionally observed that the differences between the average accuracies were quite small, where the decision between the first- and second-best techniques was determined by the third decimal place of the value of the metric. When comparing approaches in the context of DL, it is always advisable to take scalability into account. With 1000 images, a 0.1 percent difference is not particularly significant, but 0.1% of 10,000,000 images is. Therefore, even though some differences were very small, this was taken into account to decide which approaches were the best. In the same Section 4, transductive approaches presented better results compared to the inductive ones, with TIM emerging as the most effective technique. It is very likely that TIM's superior performance is largely due to how its loss function was designed. In TIM, the authors incorporated supervision from the support set \(S\) by combining a standard cross-entropy loss with empirical weighted mutual information between the query samples and their latent labels. This mutual information consists of two components. The first is a Monte Carlo estimate of the conditional entropy of the labels given the raw query features, aimed at minimising the uncertainty of the posteriors at unlabelled query samples and encouraging confident model outputs. The second component, a label-marginal entropy regulariser, promotes a uniform marginal distribution of labels, helping to avoid degenerate solutions that could arise from minimising only the conditional entropy. By analysing the results to answer RQ_2, a particular approach showed some unstable behaviour. With the XAI4 SAR dataset, PT-MAP decreased its performance in all situations when the number of epochs was increased, considering both types of tasks. The most significant decreases here occurred when the number of epochs increased from 10 to 50, with a drop of 20.229% in 1-shot tasks and 26.811% in 5-shot tasks. There was also a performance drop with the RESISC-45 dataset with 1-shot tasks when the number of epochs increased to 50, as well as in the case of the AID dataset considering 1-shot tasks, when the number of epochs reached 200. However, the worst performance was certainly when the number of epochs varied from 10 to 200 in the RESISC-45 dataset. In this case, for 1-shot tasks, the average accuracy dropped by 41.059% and 22.198% for 5-shot tasks. As can be seen in Table 9, the average accuracies of the PT-MAP approach were the worst considering all datasets, epochs, and techniques. One possible explanation for this unstable behaviour of PT-MAP is the assumption of data distribution. The approach relies on preprocessing feature vectors to fit a Gaussian-like distribution. This assumption may not hold true for all datasets or tasks, potentially limiting the technique's applicability in real-world scenarios where data distributions can be complex and varied. Also, the results presented in their article [44] show some sensitivity to hyperparameter tuning, which may have affected its performance. Finally, it is important to mention that the decision was to go up to the end of the number of epochs with no early stopping defined. Thus, it is possible that validation overfitting happened when the best model was saved multiple times during the training step and, thus, the model might begin to learn patterns specific to the validation set that do not generalise well to the test dataset. Even if no hyperparameter tuning was accomplished, this possibility might not be discarded. Table 11 exhibits a decrease in the average accuracy for the XAI4 SAR dataset when the number of epochs increased to 50, considering both 1-shot and 5-shot tasks. In this case, not only the PT-MAP technique but all approaches presented this decrease in both tasks. By increasing to 100 and 200 epochs, there was a positive but not very significant gain. SAR images are quite different from optical imagery.