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Table of contents
1.
Introduction
2.
What are Conditional GANs?
2.1.
Class-conditional GANs. 
2.2.
Inter-class Knowledge Transfer.
2.3.
Transfer Learning in GANs
3.
Applications of Conditional GANs
4.
Frequently Asked Questions
4.1.
What is Convolutional Neural Network?
4.2.
What is the pooling layer?
4.3.
What is the Convolutional layer?
5.
Conclusion
Last Updated: Mar 27, 2024

Introduction to Conditional GANs

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Speaker
Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM

Introduction

Generative adversarial networks (GANs) are the standard image and video generation models. Showing promising results in unconditional and conditional setups.

In recent years, deep learning has proven to be an extremely useful tool for discriminative tasks, through layers of linear transforms combined with nonlinearities. These systems learn to transform their input. Into an ideal representation across which we can draw clear decision boundaries.

Introduction to Conditional GANs

We show positive results using the incorporated dependent data. To control particular attributes of faces sampled from the model deterministically. We also demonstrate that the model benefits from this external input data. Using such conditional data as “priors” on generation. The model can better navigate the space of possible outputs.

In this article, we will learn about the conditional generative adversarial network.

What are Conditional GANs?

Prior and concurrent works have conditioned GANs on discrete labels, text, and images. The image-conditional models have been tackled. Image prediction from a normal map future frame prediction. Product photo generation and image generation from sparse annotations. 

Several other papers have also used GANs. For image-to-image mappings, only apply the GAN. Unconditionally, relying on different terms to force the output. To be conditioned on the input. 

These papers have achieved impressive results. Like in inpainting, future state prediction, and image manipulation. Guided by user constraints, style transfer, and superresolution. 

Each of the methods was tailored for a specific application. Our framework differs in that everything is application-specific. This makes our setup considerably more straightforward than most others.

What are Conditional GANs?

Class-conditional GANs. 

Different architectures and loss functions have been proposed. For conditioning GANs on class labels. The current state-of-the-art methods for class conditioning. Commonly employ cGAN with projection discriminator. 

The generator applies each layer's conditional batch normalization with the class-specific scale and shift parameters. 

Conversely, the discriminator is conditioned on the class labels. By computing the last feature layer's dot product. And the desired class's learnable embedding. 

The performance of the conditional GANs was further improved by adding self-attention layers to the generator and the discriminator.

Inter-class Knowledge Transfer.

A lot of work has been emerging on extending models. Trained in prior examples/images to perform favorably on new data. And here is where knowledge transfer becomes essential. 

For instance, memory and attention modules transfer. Labeled data knowledge to a recent class example.

Transfer Learning in GANs

Iterative image generation approaches, such as DGN-AM and PPGN. It could be considered early attempts at transfer learning. In image generation, by generating images via maximizing. The activation of the neurons of a pre-trained classifier. 

TransferGAN is one of the earliest studies addressing transfer learning in GANs. The authors showed that they could outperform. Training from scratch regarding image quality. And convergence time by simply fine-tuning. A pre-trained network on the target dataset. 

However, naive fine-tuning on small data still suffers from mode collapse and training instability. Another method proposes transferring the low-level layers of the generator. And the discriminator from the pre-trained network. In comparison, learning the high-level layers from scratch for the target data. 

A recent study shows that simply freezing. The low-level filters of the discriminator are more effective than previous fine-tuning approaches.

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Applications of Conditional GANs

  • Image-to-Image Translation: Convert images from one domain to another (e.g., maps to satellite images).
     
  • Text-to-Image Synthesis: Develop ideas based on textual descriptions.
     
  • Style Transfer: Apply the style of one painting to another.
     
  • Super-Resolution: Create high-resolution images from low-resolution inputs.
     
  • Image Inpainting: Fill in missing parts of an image.
     
  • Face Aging/Deaging: Simulate how a face might look in the future or the past.
     
  • Image Manipulation: Change specific attributes in images.
     
  • Custom Clothing Designs: Generate new clothing designs based on input conditions.
     
  • Data Augmentation: Expand datasets with new, consistent samples.
     
  • Drug Discovery: Design molecular structures for potential drugs.
     
  • Medical Image Synthesis: Create synthetic medical images for training models.
     
  • Environmental Simulation: Simulate changes in landscapes or urban settings.

Frequently Asked Questions

What is Convolutional Neural Network?

A convolutional neural network is a multi-layer neural network. Composed of neurons with self-learning weights and biases, each neuron receives. Some input and does some dot product calculations. And the output is each classification.

What is the pooling layer?

The pooling layer is also known as the downsampling layer. Pooling mainly reduces the amount of computation, by aggregating features and reducing dimensionality. Generally speaking, features with large dimensions. It will be obtained after convolution, which requires much analysis.

What is the Convolutional layer?

Convolutional layer, each layer of the convolutional neural network. Consists of several convolutional units. And each convolutional unit is optimized by the back-propagation algorithm. The convolution calculation aims to extract different features of the input from each layer.

Conclusion

Conditional GAN transfer by transferring the knowledge. Across both source and target classes. We represented the knowledge of individual courses.

Conditional adversarial networks are promising for many image-to-image translation tasks. Especially those involving highly structured graphical outputs. These networks learn a loss adapted. To the study and data at hand, making them applicable in various settings.

To better understand the topic, refer to 

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