2 papers
cs.CV2024
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…
cs.CV2024
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…