ParticleAugment: Sampling-Based Data Augmentation

• URL: http://arxiv.org/abs/2106.08693v1
• Date: Wed, 16 Jun 2021 10:56:02 GMT
• Title: ParticleAugment: Sampling-Based Data Augmentation
• Authors: Alexander Tsaregorodtsev, Vasileios Belagiannis
• Abstract summary: We propose a particle filtering formulation to find optimal augmentation policies and their schedules during model training. We show that our formulation for automated augmentation reaches promising results on CIFAR-10, CIFAR-100, and ImageNet datasets.
• Score: 80.44268663372233
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present an automated data augmentation approach for image classification. We formulate the problem as Monte Carlo sampling where our goal is to approximate the optimal augmentation policies. We propose a particle filtering formulation to find optimal augmentation policies and their schedules during model training. Our performance measurement procedure relies on a validation subset of our training set, while the policy transition model depends on a Gaussian prior and an optional augmentation velocity parameter. In our experiments, we show that our formulation for automated augmentation reaches promising results on CIFAR-10, CIFAR-100, and ImageNet datasets using the standard network architectures for this problem. By comparing with the related work, we also show that our method reaches a balance between the computational cost of policy search and the model performance.

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