Improving performance of deep learning models and reducing their training times are ongoing challenges in deep neural networks.There are several approaches proposed to address these challenges,one of which is to incre...Improving performance of deep learning models and reducing their training times are ongoing challenges in deep neural networks.There are several approaches proposed to address these challenges,one of which is to increase the depth of the neural networks.Such deeper networks not only increase training times,but also suffer from vanishing gradients problem while training.In this work,we propose gradient amplification approach for training deep learning models to prevent vanishing gradients and also develop a training strategy to enable or disable gradient amplification method across several epochs with different learning rates.We perform experiments on VGG-19 and Resnet models(Resnet-18 and Resnet-34),and study the impact of amplification parameters on these models in detail.Our proposed approach improves performance of these deep learning models even at higher learning rates,thereby allowing these models to achieve higher performance with reduced training time.展开更多
基金supported in part by an NVIDIA Academic Hardware Grant
文摘Improving performance of deep learning models and reducing their training times are ongoing challenges in deep neural networks.There are several approaches proposed to address these challenges,one of which is to increase the depth of the neural networks.Such deeper networks not only increase training times,but also suffer from vanishing gradients problem while training.In this work,we propose gradient amplification approach for training deep learning models to prevent vanishing gradients and also develop a training strategy to enable or disable gradient amplification method across several epochs with different learning rates.We perform experiments on VGG-19 and Resnet models(Resnet-18 and Resnet-34),and study the impact of amplification parameters on these models in detail.Our proposed approach improves performance of these deep learning models even at higher learning rates,thereby allowing these models to achieve higher performance with reduced training time.
