Effective Data Augmentation with Multi-Domain Learning GANs

by   Shin'ya Yamaguchi, et al.

For deep learning applications, the massive data development (e.g., collecting, labeling), which is an essential process in building practical applications, still incurs seriously high costs. In this work, we propose an effective data augmentation method based on generative adversarial networks (GANs), called Domain Fusion. Our key idea is to import the knowledge contained in an outer dataset to a target model by using a multi-domain learning GAN. The multi-domain learning GAN simultaneously learns the outer and target dataset and generates new samples for the target tasks. The simultaneous learning process makes GANs generate the target samples with high fidelity and variety. As a result, we can obtain accurate models for the target tasks by using these generated samples even if we only have an extremely low volume target dataset. We experimentally evaluate the advantages of Domain Fusion in image classification tasks on 3 target datasets: CIFAR-100, FGVC-Aircraft, and Indoor Scene Recognition. When trained on each target dataset reduced the samples to 5,000 images, Domain Fusion achieves better classification accuracy than the data augmentation using fine-tuned GANs. Furthermore, we show that Domain Fusion improves the quality of generated samples, and the improvements can contribute to higher accuracy.


page 8

page 11

page 12

page 13

page 14

page 15


SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images

Recently deep learning methods, in particular, convolutional neural netw...

Unconstrained Road Marking Recognition with Generative Adversarial Networks

Recent road marking recognition has achieved great success in the past f...

Improving the Performance of Fine-Grain Image Classifiers via Generative Data Augmentation

Recent advances in machine learning (ML) and computer vision tools have ...

Teaching a GAN What Not to Learn

Generative adversarial networks (GANs) were originally envisioned as uns...

Diffusion Probabilistic Models beat GANs on Medical Images

The success of Deep Learning applications critically depends on the qual...

Label Geometry Aware Discriminator for Conditional Generative Networks

Multi-domain image-to-image translation with conditional Generative Adve...

Please sign up or login with your details

Forgot password? Click here to reset