Method for Whale Re-identification Based on Siamese Nets and Adversarial Training

Training Convolutional Neural Networks that do well in one-shot learning settings can have wide range of impacts on real-world datasets. In this paper, we explore an adversarial training method that learns a Siamese neural network in an end-to-end fashion for two models—ConvNets model that learns image embeddings from input image pair, and head model that further learns the distance between those embeddings. We further present an adversarial mining approach that efficiently identifies and selects harder examples during training, which significantly boosts the model performance over difficult cases. We have done a comprehensive evaluation using a public whale identification dataset hosted on Kaggle platform, and report state-of-the-art performance benchmarking against result of other 2000 participants.