  The Google-Landmarks-v2 dataset is the biggest worldwide landmarks dataset
characterized by a large magnitude of noisiness and diversity. We present a
novel landmark retrieval/recognition system, robust to a noisy and diverse
dataset, by our team, smlyaka. Our approach is based on deep convolutional
neural networks with metric learning, trained by cosine-softmax based losses.
Deep metric learning methods are usually sensitive to noise, and it could
hinder to learn a reliable metric. To address this issue, we develop an
automated data cleaning system. Besides, we devise a discriminative re-ranking
method to address the diversity of the dataset for landmark retrieval. Using
our methods, we achieved 1st place in the Google Landmark Retrieval 2019
challenge and 3rd place in the Google Landmark Recognition 2019 challenge on
Kaggle.
