Deep Learning – Deep Convolutional Generative Adversarial Networks Basics

Deep Convolutional Generative Adversarial Networks- Sir. Yann LeCun, Facebook’s AI research director made a very intuitive and interesting comment about GANs “Adversarial training “The most interesting idea in the last 10 years in the field of Machine Learning.”

Generative Adversarial Networks (GANs) – A combination of two neural networks which is a very effective generative model network, works simply opposite to neural nets. Usually, neural network models take complex input and provide simple output but in GANs it’s just the opposite. GANs are a very young family member of Deep Neural Network Architecture. Introduced by Ian Goodfellow and his team at the University of Montreal in 2014. GANs are a class of unsupervised machine learning algorithms. In our previous post, “Deep Learning – Introduction to GANs”. you and I got introduced to the basic analogy, concept, and ideas behind “How GANs work”. In this article, we will explore and discuss our intuitive explanation of deep convolutional generative adversarial networks (DCGANs) on a high level and in simple language.

Let’s do a small recap about GANs

Generative Adversarial Networks are a class of algorithms used in the unsupervised learning environment. As the name suggests they are called Adversarial Networks because they are made up of two competing neural networks.

Both networks compete with each other to achieve a zero-sum game. Both neural networks are assigned different job roles i.e. contesting with each other.

The process in GANs involves automatically learning to discover the regularities or patterns in input data. GANs can also generate photorealistic images the main idea behind DCGANs

• Neural Network One is called the Generator because it generates new data instances. The generator’s loss quantifies how well it was able to trick the discriminator
• Another neural net called a Discriminator, evaluates work for the first neural net for authenticity.

GANs are revolutionary models in machine learning that excel at generating new data samples. One of their key functionalities lies in their ability to transform samples from a simple distribution, like a uniform or normal distribution, into samples that closely resemble the distribution of certain datasets. This process allows GANs to produce accurate or near-perfect results.

To understand GANs better, let’s consider a scenario from everyday life. Imagine me and my son who is much better at playing chess than my self. If my goal is to improve my chess skills, one effective approach could be to play against my son regularly. By engaging in these matches and learning from his strategies and moves, I can gradually enhance my own skills and become a better player.

Additionally, GANs have a wide range of applications across various domains. Two notable examples include:

1. Image Generation: GANs can generate photorealistic images by learning from a dataset of images. They can create diverse and high-quality images of human faces, bedrooms, landscapes, and more, making them valuable tools for artists, designers, and content creators.
2. Image Editing: Another application of GANs is in image-to-image translation tasks, allowing for tasks such as style transfer, image super-resolution, and colorization. These capabilities enable users to manipulate and enhance images in creative ways, opening up possibilities for innovative visual content creation.

These examples highlight the versatility and practicality of GANs in various domains, showcasing their potential to transform data generation and manipulation tasks.

Small recap about CNNs

Convolutional neural network – CNNs are inspired by the structure of the brain but our focus will not be on neural science here as we do not have any expertise or academic knowledge in any of the biological aspects. We are going artificial in this post. CNNs are a class of Neural Networks that have proven very effective in areas of image recognition, processing, and classification.

CNNs Might look or appears like magic to many but in reality, it’s just simple science and mathematics only. CNNs are a class of neural networks that have proven very effective in areas of image recognition thus in most cases it’s applied to image processing.

CNNs got huge adoption and success within computer vision applications but mainly it is with supervised learning as compared with unsupervised learning which has got very little attention.

This network is a great example of variation for multilayer perceptron for processing and classification. It’s a deep learning algorithm in which it takes input as an image and put weights and biases effectively to its objects and is finally able to differentiate images from each other.

According to AILabPage, a Convolutional Neural Network (CNN) is a specialized multi-layer neural network designed for image processing and recognition tasks. Leveraging advanced computing power, CNNs automatically extract hierarchical features from raw input images through convolutional layers, minimizing the need for extensive preprocessing. This streamlined approach benefits businesses by accelerating image-based tasks such as product recognition, enhancing data-driven decision-making processes, and optimizing computational resources.

As per Wiki – In machine learning, a convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networks, most commonly applied to analysing visual imagery. Deep learning trains models with many layers.

They exist already for several decades but were shown to be very powerful when large labelled datasets are used. This requires fast computers (e.g. GPUs)!

Artificial Intelligence solutions behind CNN amazingly transform how businesses and developers create user experiences and solve real-world problems. CNNs are also known as the application of neuroscience to machine learning. They employ mathematical operations known as “Convolution”; which is a specialised kind of linear operation.

• Image Processing
  • Recognition
  • Classification
  • Video labeling
  • Text analysis,
• Speech Recognition
  • Natural language processing
  • Text classification processing

Convolutional Neural Networks applications solve many unsolved problems that could remain unsolved without convolutional neural networks with many layers, including high calibres AI systems such as AI-based robots, virtual assistants, and self-driving cars. Other common applications where CNNs are used as mentioned above like emotion recognition and estimating age/gender etc The best-known models are convolutional neural networks and recurrent neural networks

Deep Convolutional Generative Adversarial Networks

GANs work with image data sets and use Convolutional Neural Networks, as the generator and discriminator models. The data model based on deep convolutional GANs (DCGAN) can be introduced by borrowing the convolutional architecture that has been proven extremely successful for discriminative computer vision problems.

With the scope of this post, we’ll try to explain how GANs can be used to generate photorealistic images. Also, will help to bridge the gap between the success of CNNs for supervised learning and unsupervised learning.

The DCGAN architecture (at the core level) uses a standard CNN architecture for the discriminative model where for the generator, convolutions are replaced with up-convolutions. The class of CNNs i.e. deep convolutional generative adversarial networks (DCGANs), with special constraints and architecture, prove themself a very strong contender to disrupt CNNs in the unsupervised learning domain.

Enhanced Object Diagram for Deep Convolutional Generative Adversarial Networks

• Generator and Discriminator Classes – Include a loss_function attribute representing the loss function used for training.
• Methods to set the loss function, learning rate, and batch size have been added to the Generator, Discriminator, and DCGAN classes for flexibility in configuration.
• The LossFunction class represents different types of loss functions used for training, and the calculate_loss method calculates the loss between predicted and true values.
• Relationships between objects are depicted with arrows to illustrate dependencies and interactions.

How convolutional neural networks can be useful and can be extended for generating photorealistic images by combing a bit of logic of GANs on top of CNN. At the generator level, the representation at each layer is actually successively very large.

Components of a DCGAN

Deep Convolutional Generative Adversarial Networks (DCGANs) are powerful models in the field of deep learning, particularly for generating synthetic data. Understanding the components and interactions within a DCGAN system is crucial for developing and deploying effective generative models

• Generator– Responsible for generating synthetic data, such as images, based on random noise inputs.
  • It consists of filters, biases, optimizer, input and output shapes, loss function, activation function, and regularization techniques.
  • Example: Generating realistic images of human faces from random noise inputs.
• Discriminator – Evaluates the authenticity of generated data by distinguishing between real and synthetic samples.
  • It shares similar attributes with the Generator but with an additional discriminative functionality.
  • Example: Distinguishing between real and generated images in an image dataset.
• Image – Represented as objects containing pixel data, width, and height attributes.
  • They serve as input and output data for both the Generator and Discriminator.
  • Example: RGB images of various objects in a dataset.
• Dataset – The Dataset class manages collections of images used for training and evaluation.
  • It includes methods for adding images and retrieving random batches for training.
  • Example: A dataset of hand-written digits for training a digit generator.
• DCGAN – A class orchestrates the training process by coordinating interactions between the Generator, Discriminator, and Dataset.
  • It handles parameters such as learning rate and batch size for optimization.
  • Example: Training a DCGAN to generate photorealistic landscapes.
• Optimizer – It adjust the weights of the Generator and Discriminator during training to minimize the loss function.
  • They update network parameters based on gradients computed during backpropagation.
  • Example: Stochastic Gradient Descent (SGD) optimizer for updating network weights.
• Loss Function – It quantify the discrepancy between predicted and ground truth data.
  • They guide the training process by providing feedback on model performance.
  • Example: Binary cross-entropy loss for binary classification tasks.
• Activation Function – It introduce non-linearity into neural networks, enabling them to learn complex patterns.
  • They transform input data to output data within each layer of the network.
  • Example: Rectified Linear Unit (ReLU) activation function for introducing non-linearity.
• Regularization – These techniques prevent overfitting by adding constraints to the network’s weights.
  • They improve the generalization of the model to unseen data.
  • Example: L2 regularization for penalizing large weights in the network.

Interactions within a DCGAN System

In a DCGAN system, the Generator produces synthetic data, which the Discriminator evaluates for authenticity. The Optimizer updates weights based on loss computations, while the Dataset supplies real data for training and evaluation purposes.

• The Generator and Discriminator interact with each other through the DCGAN class, exchanging data and updating parameters based on optimization algorithms.
• Training involves iterating over batches of data from the Dataset, generating synthetic samples with the Generator, evaluating them with the Discriminator, and updating the network weights accordingly.

The idea is to do the mapping from a low-dimensional latent vector onto a high-dimensional image. CNN’s architecture can be leveraged by GANs to generate photorealistic images. The reasons are simple as CNNs are so successful for discriminative computer vision problems.

Conclusion: In this post, we have learned some high-level basics of GANs- Generative Adversarial Networks. GANs are recent development efforts but look very promising and effective for a real-life business use case. One thing to here the two networks G &D are designed to contest and not work against to pull others down. Both work together to achieve something big. The discriminator helps and teaches the generator with constant feedback and gives an indirect suggestion of what to adjust, this process trains the generator well and strongly. Commercial models of GANs are out but still, they are in the research phase as we get new variants quite frequently.

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Points to Note:

All credits if any remain on the original contributor only. We have covered all basics around the Generative Neural Network. Though often such tasks struggle to find the best companion between CNNs and RNNs algorithms to look for information.

Books + Other material Referred

• Research through open internet, news portals, white papers and imparted knowledge via live conferences & lectures.
• Lab and hands-on experience of @AILabPage (Self-taught learners group) members.
• NIPS 2016 Tutorial: GANs
• Generative Adversarial Networks
• Unsupervised Representation Learning with Deep Convolutional GAN

Feedback & Further Question

Do you have any questions about Deep Learning or Machine Learning? Leave a comment in the comment section or ask your question via email. Will try my best to answer it.

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