scholar.google.com › citations
Drop-Activation: Implicit Parameter Reduction and Harmonic Regularization. Overfitting frequently occurs in deep learning. In this paper, we propose a novel regularization method called Drop-Activation to reduce overfitting and improve generalization.
Nov 14, 2018
PDF | Overfitting frequently occurs in deep learning. In this paper, we propose a novel regularization method called Drop-Activation to reduce.
Drop-Activation is a regularization method to reduce the risk of overfitting. The key idea is to drop nonlinear activation functions by setting them to be ...
The experimental results show that drop-activation generally improves the performance of popular neural network architectures for the image classification ...
Nov 15, 2018 · Overfitting frequently occurs in deep learning. In this paper, we propose a novel regularization method called Drop-Activation to reduce ...
Mar 27, 2020 · This verifies that the original network has been over-parametired and Drop-Activation can regularize the network by implicit parameter reduction ...
Missing: Harmonic | Show results with:Harmonic
Overfitting frequently occurs in deep learning. In this paper, we propose a novel regularization method called Drop-Activation to reduce overfitting and ...
Abstract: Overftting frequently occurs in deep learning. In this paper, we propose a novel regularization method called drop-activation to reduce overftting and ...
Missing: Harmonic | Show results with:Harmonic
ShakeDrop is inspired by Shake-Shake regularization that decreases error rates by disturbing learning and can be applied to not only ResNeXt but also ResNet ...
... Parameter Reduction And Harmonic Regularization", ARXIV-CS.LG, 2018. ... al., 2018) propose a novel regularization method called Drop-Activation to reduce ...