  This paper presents the Axon AI's solution to the 2nd YouTube-8M Video
Understanding Challenge, achieving the final global average precision (GAP) of
88.733% on the private test set (ranked 3rd among 394 teams, not considering
the model size constraint), and 87.287% using a model that meets size
requirement. Two sets of 7 individual models belonging to 3 different families
were trained separately. Then, the inference results on a training data were
aggregated from these multiple models and fed to train a compact model that
meets the model size requirement. In order to further improve performance we
explored and employed data over/sub-sampling in feature space, an additional
regularization term during training exploiting label relationship, and learned
weights for ensembling different individual models.
