engleski

Effect of Data Augmentation Methods on Face Image Classification Results

Data augmentation encompasses a set of techniques to increase the size of a dataset artificially. Insufficient training data means that the network will be susceptible to the problem of overfitting, leading to a poor generalization capability of the network. Therefore, research efforts are focused on developing various augmentation strategies. Simple affine transformations are commonly used to expand a set. However, more advanced methods, such as information dropping or random mixing, are becoming increasingly popular. We analyze different data augmentation techniques suitable for the image classification task in this paper. We investigate how the choice of a particular approach affects the classification results depending on the size of the training dataset, the type of transfer learning applied, and the task's difficulty, which we determine based on the objectivity or subjectivity of the target attribute. Our results show that the choice of augmentation method becomes crucial in th e case of more challenging tasks, especially when using a pre-trained model as a feature extractor. Moreover, the methods that showed above-average results on smaller sets may not be the optimal choice on a larger set and vice versa.

Data Augmentation, Image Classification, Transfer Learning

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