Naslov
LSUN-Stanford Car Dataset: Enhancing Large- Scale Car Image Datasets Using Deep Learning for Usage in GAN Training

Autori
Kramberger, Tin ; Potočnik, Božidar

Izvornik
Applied Sciences-Basel (2076-3417) 10 (2020), 14; 1, 12

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, ostalo

Ključne riječi
GAN dataset ; car image dataset ; Generative Adversarial Network ; automotive image dataset ; GAN neural network

Sažetak
Currently there is no publicly available adequate dataset that could be used for training Generative Adversarial Networks (GANs) on car images. All available car datasets differ in noise, pose, and zoom levels. Thus, the objective of this work was to create an improved car image dataset that would be better suited for GAN training. To improve the performance of the GAN, we coupled the LSUN and Stanford car datasets. A new merged dataset was then pruned in order to adjust zoom levels and reduce the noise of images. This process resulted in fewer images that could be used for training, with increased quality though. This pruned dataset was evaluated by training the StyleGAN with original settings. Pruning the combined LSUN and Stanford datasets resulted in 2, 067, 710 images of cars with less noise and more adjusted zoom levels. The training of the StyleGAN on the LSUN-Stanford car dataset proved to be superior to the training with just the LSUN dataset by 3.7% using the Fréchet Inception Distance (FID) as a metric. Results pointed out that the proposed LSUN-Stanford car dataset is more consistent and better suited for training GAN neural networks than other currently available large car datasets.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

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