The field of image-to-image translation consists of learning a transformation of an image from one domain to another. It has experienced great success during recent years, with methods being able to generate realistic outputs when converting between multiple categorical domains at once. However, existing approaches have not yet been extended to ordinal domains. Therefore, this thesis investigates how existing image-to-image translation methods can be extended to use ordinal labels and introduces the Ordinal GAN (OrGAN) architecture as one possible solution to the problem. OrGAN is based on two fundamental modifications to existing methods, namely, adding Gaussian noise to the labels of the data samples in each iteration of training and usin...
Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably...
Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably...
Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably...
The field of image-to-image translation consists of learning a transformation of an image from one d...
Generative Adversarial Networks (GANs) have been extremely successful in various application domains...
Generative Adversarial Networks (GANs) have been extremely successful in various application domains...
Generative Adversarial Networks (GANs) have been extremely successful in various application domains...
Generative Adversarial Networks (GANs) have been extremely successful in various application domains...
In this burgeoning age and society where people are tending towards learning the benefits adversaria...
Image-to-Image translation is a collection of computer vision problems that aim to learn a mapping b...
Image-to-Image translation is a collection of computer vision problems that aim to learn a mapping b...
Data augmentation is a technique that acquires more training data by augmenting available samples, w...
Data augmentation is a technique that acquires more training data by augmenting available samples, w...
Data augmentation is a technique that acquires more training data by augmenting available samples, w...
Generative machine learning models make it possible to derive new data from a dataset. There are man...
Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably...
Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably...
Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably...
The field of image-to-image translation consists of learning a transformation of an image from one d...
Generative Adversarial Networks (GANs) have been extremely successful in various application domains...
Generative Adversarial Networks (GANs) have been extremely successful in various application domains...
Generative Adversarial Networks (GANs) have been extremely successful in various application domains...
Generative Adversarial Networks (GANs) have been extremely successful in various application domains...
In this burgeoning age and society where people are tending towards learning the benefits adversaria...
Image-to-Image translation is a collection of computer vision problems that aim to learn a mapping b...
Image-to-Image translation is a collection of computer vision problems that aim to learn a mapping b...
Data augmentation is a technique that acquires more training data by augmenting available samples, w...
Data augmentation is a technique that acquires more training data by augmenting available samples, w...
Data augmentation is a technique that acquires more training data by augmenting available samples, w...
Generative machine learning models make it possible to derive new data from a dataset. There are man...
Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably...
Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably...
Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably...