Balazs Gerofi

KAKURENBO: Adaptively Hiding Samples in Deep Neural Network Training

This paper proposes a method for hiding the least-important samples during the training of deep neural networks to increase efficiency, i.e., to reduce the cost of training. Using information about the loss and prediction confidence during training, we adaptively find samples to exclude in a given e…