부산대학교 도서관

As Google has made its large-scale dataset available to the public, innovation in the field of Google Landmark Classifications has taken a forefront for research in field of Deep Learning. The Google dataset contains photos of different landmarks aggregated over a period of time. The contributions are made by millions of people for thousands of monuments worldwide. The dataset is large and is unbalanced posing problems to researchers for building models. This study demonstrates the application of transfer learning in conjunction with data augmentation to produce a model that yields 82.03% Top-5 accuracy on a modified version of the original Google-Landmarks dataset, which comprises pictures from 6,151 distinct landmarks. We also discuss the application of a Generative Adversarial Network in mitigating the issues caused by an exceedingly unbalanced