This is the Linux app named Mixup-CIFAR10 whose latest release can be downloaded as mixup-cifar10sourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named Mixup-CIFAR10 with OnWorks for free.
Suivez ces instructions pour exécuter cette application :
- 1. Téléchargé cette application sur votre PC.
- 2. Entrez dans notre gestionnaire de fichiers https://www.onworks.net/myfiles.php?username=XXXXX avec le nom d'utilisateur que vous voulez.
- 3. Téléchargez cette application dans ce gestionnaire de fichiers.
- 4. Démarrez l'émulateur en ligne OnWorks Linux ou Windows en ligne ou l'émulateur en ligne MACOS à partir de ce site Web.
- 5. Depuis le système d'exploitation OnWorks Linux que vous venez de démarrer, accédez à notre gestionnaire de fichiers https://www.onworks.net/myfiles.php?username=XXXXX avec le nom d'utilisateur que vous souhaitez.
- 6. Téléchargez l'application, installez-la et exécutez-la.
Mixup-CIFAR10
Ad
DESCRIPTION
mixup-cifar10 is the official PyTorch implementation of “mixup: Beyond Empirical Risk Minimization” (Zhang et al., ICLR 2018), a foundational paper introducing mixup, a simple yet powerful data augmentation technique for training deep neural networks. The core idea of mixup is to generate synthetic training examples by taking convex combinations of pairs of input samples and their labels. By interpolating both data and labels, the model learns smoother decision boundaries and becomes more robust to noise and adversarial examples. This repository implements mixup for the CIFAR-10 dataset, showcasing its effectiveness in improving generalization, stability, and calibration of neural networks. The approach acts as a regularizer, encouraging linear behavior in the feature space between samples, which helps reduce overfitting and enhance performance on unseen data.
Comment ça marche
- Simple, easily extensible codebase for research and experimentation
- Based on the original ICLR 2018 publication results
- Compatible with PyTorch and GPU-accelerated training
- Demonstrates significant gains in generalization and robustness
- Trains neural networks on convex combinations of inputs and labels
- Implementation of mixup data augmentation for CIFAR-10 classification
Langage de programmation
Python
Catégories
This is an application that can also be fetched from https://sourceforge.net/projects/mixup-cifar10.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
