Papers › Generalizing MLPs With Dropouts, Batch Normalization, and Skip Connections
Generalizing MLPs With Dropouts, Batch Normalization, and Skip Connections
Taewoon Kim
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/
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Age And Gender Classification | Adience Age | RetinaFace + ArcFace + MLP + IC + Skip connections | Accuracy (5-fold) | 60.86 | #9 of 16 | Archive leaderboard | report |
| Age And Gender Classification | Adience Gender | RetinaFace + ArcFace + MLP + Skip connections | Accuracy (5-fold) | 90.66 | #4 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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