Beyond CNNs: Encoded Context for Image Inpainting with LSTMs and Pixel CNNs

Ayesha Noor Taneem Ullah Jan
Additional contact information
Ayesha Noor Taneem Ullah Jan: Computer Science & IT University of Engineering and Technology Peshawar, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 5, 165–179

Abstract: Our paper presents some creative advancements in the image in-painting techniques for small, simple images for example from the CIFAR10 dataset. This study primarily targeted on improving the performance of the context encoders through the utilization of several major training methods on Generative Adversarial Networks (GANs). To achieve this, we upscaled the network Wasserstein GAN (WGAN) and compared the discriminators and encoders with the current state-of-the-art models, alongside standard Convolutional Neural Network (CNN) architectures. Side by side to this, we also explored methods of Latent Variable Models and developed several different models, namely Pixel CNN, Row Long Short Term Memory (LSTM), and Diagonal Bidirectional Long Short-Term Memory (BiLSTM). Moreover, we proposed a model based on the Pixel CNN architectures and developed a faster yet easy approach called Row-wise Flat Pixel LSTM. Our experiments demonstrate that the proposed models generate high-quality images on CIFAR10 while conformingthe L2 loss and visual quality measurement

Keywords: Index Terms—Image Inpainting; GAN; Pixel CNN; LSTM (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journal.50sea.com/index.php/IJIST/article/view/783/1369 (application/pdf)
https://journal.50sea.com/index.php/IJIST/article/view/783 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:abq:ijist1:v:6:y:2024:i:5:p:165-179

Access Statistics for this article

International Journal of Innovations in Science & Technology is currently edited by Prof. Dr. Syed Amer Mahmood

More articles in International Journal of Innovations in Science & Technology from 50sea
Bibliographic data for series maintained by Iqra Nazeer ().