Animal species classification using deep neural networks with noise labels.

In this paper, we developed a robust learning method for animal classification from camera-trap images collected in highly cluttered natural scenes and annotated with noisy labels. We proposed two different network structures with and without clean samples to handle noisy labels. We use k-means clustering to divide the training samples into groups with different characteristics, which are then used to train different networks. These networks with enhanced diversity are then used to jointly predict or correct sample labels using max voting. We evaluate the performance of the proposed method on two public available camera-trap image datasets: Snapshot Serengeti and Panama-Netherlands datasets. Our experimental results demonstrate that our method outperforms the state-of-the-art methods from the literature and achieved improved accuracy on animal species classification from camera-trap images with high levels of label noise. • Dealing with label noise in camera-trap images. • Partitioning noisy training set to train the independent networks. • Using multiple stages to retrain the networks on the updated labels. [ABSTRACT FROM AUTHOR]