This research provides an overview on how training Convolutional Neural Networks (CNNs) on imbalanced datasets affect the performance of the CNNs. Datasets could be imbalanced as a result of several reasons. There are for example naturally less samples of rare diseases. Since the network is trained less on those instances, this might lead to worse performance on those cases. However, it might be more crucial to identify those cases properly. Furthermore, it is non-trivial to check whether real-time generated data is balanced. The networks in this research are trained on three different types of synthetic datasets. Balanced datasets, datasets with missing targets and datasets that have normally distributed targets. The task of the network is to find the standard deviation of the pixel intensity of the input. The results show that it is best to train the network on balanced datasets, however training networks on datasets with normally distributed targets does not result in a big loss. Furthermore, in this case the CNNs were still able to learn the task with decent performance if the training set missed targets.

Does a convolutional neural network (CNN) always have to be deep to learn a task? This is an important question as deeper networks are generally harder to train. We trained shallow and deep CNNs and evaluated their performance on simple regression tasks, such as computing the mean pixel value of an image. For these simple tasks we show that going deeper does not guarantee an improvement in performance.

With an expectation of 8.3 trillion photos stored in 2021 [1], convolutional neural networks (CNN) are beginning to be preeminent in the field of image recognition. However, with this deep neural network (DNN) still being seen as a black box, it is hard to fully employ its capabilities. A need to tune hyperparameters is required to have a robust CNN that can more accurately do its task. In this study, the batch size, being one of the most important hyperparameters, is our main concern. The batch size is the number of samples that will be propagated through the network before updating the weights. Moreover, we show how the batch affects the performance of Regression CNNs to the following regression tasks: the mean, median, standard deviation (std) and variance of the pixel intensities of a grey-scale MNIST [2] input image. This will be analyzed by how well regression CNNs converge, given different batch sizes and a fixed learning rate. Additionally, we will also be comparing the final mean squared error given by all different batch sizes. At the end of the research, our findings concluded that a higher batch size leads to a higher Mean Squared Error (MSE) and a slower convergence. Additionally, the best performance obtained was for batch sizes of size 8 to 32, with slight differences between the four different regressions tasks.