## 3 Experiments
### Experimental Results for the Proposed Method  
We trained a model on the unlabeled data (U), and then used the trained model as a pre-trained model to fine-tune the model on the labeled data (D) with transfer learning, denoted by F(D). Three sets of experiments were performed to evaluate the proposed methods of exploiting the unlabeled data for dealing with imbalanced learning. All the experiments used ResNet-50 as the network backbone.  
The first set was trained on unlabeled data using self-supervised learning. After that, the model was fine-tuned using the learned representations, denoted by T(U)F(D). For the second experiment, the model was trained using labeled data, denoted by T(D). Then, the trained model predicted the unlabeled data to generate pseudo labels (D\({}^{\prime}\)), denoted by L(U)\(\rightarrow\)D\({}^{\prime}\). Subsequently, the model was trained on these artificially labeled data, denoted by T(D\({}^{\prime}\)). Finally, the model was fine-tuned on the labeled data. This process can be denoted by T(D)L(U)\(\rightarrow\)D\({}^{\prime}\) T(D\({}^{\prime}\))F(D). The third experiment was a combination of self-supervised learning and semi-supervised learning. Since the model, which was fine-tuned on the labeled training set, uses a pre-trained model, which was trained on the unlabeled dataset utilizing self-supervised learning, T(U)F(D) can also be used to create pseudo labels. The combined process is denoted by T(U)F(D) L(U)\(\rightarrow\)D\({}^{\prime}\) T(D\({}^{\prime}\))F(D). It should be noted that the fine-tuning of the classifier was added when fine-tuning the model on the imbalanced data in each set of these experimental processes in order to handle the imbalanced learning problem. Since re-sampling hinders representation learning, additional fine-tuning of the classifier, denoted by F(cls), was implemented on the re-balanced sampling of labeled data after fine-tuning the whole model. The detailed experimental results of the EyePaCS test dataset for each set are presented in Table 4.  
As shown in Table 4, the results of the three sets of experiments using the proposed method outperformed the ordinary supervised learning baseline. The accuracy was effectively improved by about 2-4% and 3-5% in the case of learning with imbalanced and balanced data, respectively. Additional fine-tuning of the model's classifier on the re-sampling data helped to increase this by about 1%. Although no significant increase was observed when annotating unlabeled data with the result of T(U)F(D) compared to the results obtained from the second set, the combined method made the whole process much shorter than the experimental process in the second set. Thus, it saved more computational cost, since only two iterations were needed until no new pseudo labels were generated. However, when performing the second set of experiments, more than five iterations were required. Figure 4 is a visualization of the results on a heat map generated using Grad-CAM [47] for images classified to have diabetic retinopathy. According to the image, it can be inferred that the bright yellow areas are important, compared to the surroundings, for the classification of the disease.