Sampling Strategies for GAN Synthetic Data

Abstract

Generative Adversarial Networks (GANs) have been used widely to generate large volumes of synthetic data. This data is being utilized for augmenting with real examples in order to train deep Convolutional Neural Networks (CNNs). Studies have shown that the generated examples lack sufficient realism to train deep CNNs and are poor in diversity. Unlike previous studies of randomly augmenting the synthetic data with real data, we present our simple, effective and easy to implement synthetic data sampling methods to train deep CNNs more efficiently and accurately. To this end, we propose to maximally utilize the parameters learned during training of the GAN itself. These include discriminator's realism confidence score and the confidence on the target label of the synthetic data. In addition to this, we explore reinforcement learning (RL) to automatically search a subset of meaningful synthetic examples from a large pool of GAN synthetic data. We evaluate our method on two challenging face attribute classification data sets viz. AffectNet and CelebA. Our extensive experiments clearly demonstrate the need of sampling synthetic data before augmentation, which also improves the performance of one of the state-of-the-art deep CNNs in vitro.

References in corpus (10)

• FaceNet: A Unified Embedding for Face Recognition and Clustering
• AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
• Deep Facial Expression Recognition: A Survey
• Learning from Synthetic Humans
• AutoAugment: Learning Augmentation Policies from Data
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• A Note on the Inception Score
• Do We Really Need to Collect Millions of Faces for Effective Face Recognition?
• Dataset Augmentation in Feature Space
• Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model

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• Co-supervised learning paradigm with conditional generative adversarial networks for sample-efficient classification
• Conformalised data synthesis
• Label Geometry Aware Discriminator for Conditional Generative Networks
• Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models
• Disentanglement based Active Learning