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Layout Generation and Baseline Implementation

Contents

Layout VAE

LayoutVAE is a variational autoencoder based model . It is a probabilistic and autoregressive model which generates the scene layout using latent variables in lower dimensions . It is capable of generating different layouts using the same data point.

Layout VAE Model

modelvae

Flow Diagram

Architecture

Results Obtained

VAE_result

Layout Transformer

Layout Transformer is a model proposed for generating structured layouts which can be used for documents, websites, apps, etc. It uses the decoder block of the Transformer Model, which is able to capture the relation of the document boxes with the previously predicted boxes (or inputs). Since it is an auto-regressive model, it can be used to generate entirely new layouts or to complete existing partial layouts. The paper also emphasized on the fact that this model performs better than the existing models (at that time) and is better in the following aspects:

Layout Transformer Model Architecture

Trans_model

Results

Trans_result

LayoutGAN

LayoutGAN uses a GAN network , with the generator taking randomly sampled inputs (class probabilities and geometric parameters) as parameters, arranging them and thus producing refined geometric and class parameters.

Architecture

<img src="LayoutGAN/demo/layoutgan.png" width="700" height="300">

Results on MNIST

Results on single column layouts

<img src="LayoutGAN/demo/single_col_result.png" height="787" width="473">

Quantitative Comparison

A total of three metrics were used to compare the models.

After Calculating the losses for each model, the following comparison table was obtained:

OverlapIOUAlignment
Original Data1.0000001.0000001.000000
LayoutGAN1172.0052342745.4375291.164882
LayoutVAE119.320127185.8643813.493406
Layout Transformer1.0903151.4222970.739862