1 citations · 1 across the 3 of their papers we have counts for
3 papers
LayerDropBack: A Universally Applicable Approach for Accelerating Training of Deep Networks
Evgeny Hershkovitch Neiterman, Gil Ben-Artzi
Training very deep convolutional networks is challenging, requiring significant computational resources and time. Existing acceleration methods often depend on specific architectur…
ChannelDropBack: Forward-Consistent Stochastic Regularization for Deep Networks
Evgeny Hershkovitch Neiterman, Gil Ben-Artzi
Incorporating stochasticity into the training process of deep convolutional networks is a widely used technique to reduce overfitting and improve regularization. Existing technique…
Adaptive Enhancement of Extreme Low-Light Images
Evgeny Hershkovitch Neiterman, Michael Klyuchka, Gil Ben-Artzi
Existing methods for enhancing dark images captured in a very low-light environment assume that the intensity level of the optimal output image is known and already included in the…