AN ANALYSIS ON VERY DEEP CONVOLUTIONAL NEURAL NETWORKS: PROBLEMS AND SOLUTIONS

Abstract

Neural Networks have become a powerful tool in computer vision because of the recent breakthroughs in computation time and model architecture. Very deep models allow for better deciphering of the hidden patterns in the data; however, training them successfully is not a trivial problem, because of the notorious vanishing/exploding gradient problem. We illustrate this problem on VGG models, with 8 and 38 hidden layers, on the CIFAR100 image dataset, where we visualize how the gradients evolve during training. We explore known solutions to this problem like Batch Normalization (BatchNorm) or Residual Networks (ResNets), explaining the theory behind them. Our experiments show that the deeper model su ers from the vanishing gradient problem, but BatchNorm and ResNets do solve it. The employed solutions slighly improve the performance of shallower models as well, yet, the fixed deeper models outperform them. Received by the editors: 26 January 2021. 2010 Mathematics Subject Classification. 68T45 . 1998 CR Categories and Descriptors. I.2.1 [Artificial Intelligence]: Learning – Connectionism and neural nets.