Papers › Maxout Networks
Maxout Networks
Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua Bengio
We consider the problem of designing models to leverage a recently introduced approximate model averaging technique called dropout. We define a simple new model called maxout (so named because its output is the max of a set of inputs, and because it is a natural companion to dropout) designed to both facilitate optimization by dropout and improve the accuracy of dropout's fast approximate model averaging technique. We empirically verify that the model successfully accomplishes both of these tasks. We use maxout and dropout to demonstrate state of the art classification performance on four benchmark datasets: MNIST, CIFAR-10, CIFAR-100, and SVHN.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | CIFAR-10 | Maxout Network (k=2) | Percentage correct | 90.65 | #200 of 265 | Archive leaderboard | report |
| Image Classification | CIFAR-100 | Maxout Network (k=2) | Percentage correct | 61.43 | #196 of 211 | Archive leaderboard | report |
| Image Classification | MNIST | Maxout Networks | Percentage error | 0.5 | #33 of 81 | Archive leaderboard | report |
| Image Classification | SVHN | Maxout | Percentage error | 2.5 | #36 of 62 | 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
Introduced by this paper: Maxout
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