Generalizing MLPs With Dropouts, Batch Normalization, and Skip Connections
arXiv:2108.08186
Abstract
A multilayer perceptron (MLP) is typically made of multiple fully connected layers with nonlinear activation functions. There have been several approaches to make them better (e.g. faster convergence, better convergence limit, etc.). But the researches lack structured ways to test them. We test different MLP architectures by carrying out the experiments on the age and gender datasets. We empirically show that by whitening inputs before every linear layer and adding skip connections, our proposed MLP architecture can result in better performance. Since the whitening process includes dropouts, it can also be used to approximate Bayesian inference. We have open sourced our code, and released models and docker images at https://github.com/tae898/age-gender/
arXiv comment8 pages not including references
References in corpus (5)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Decoupled Weight Decay Regularization
- Tune: A Research Platform for Distributed Model Selection and Training
- Compacting, Picking and Growing for Unforgetting Continual Learning
- Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks