Generative Adversarial Network

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21 Aug, 2024

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What Is Generative Adversarial Network (GAN)?

Generative Adversarial Network or GAN is a deep learning framework that trains two models simultaneously, a generative model and a discriminative model. The model generator and model discriminator aim to capture the distribution of target data by examining the generated data with reference to accurate data, respectively.

Creating generative models that can produce large-scale data distribution samples like images and speeches is a challenge in statistics. GAN addresses this issue and is an effective method for learning deep representations without labeled training data. It can generate large amounts of data without precise modeling of the probability density function.

What Is Generative Adversarial Network (GAN)?

Generative Adversarial Networks (GANs) are deep learning frameworks that train two models, a generator and a discriminator, simultaneously. GANs can generate large-scale data distribution samples, such as images and speeches, without requiring precise modeling of functions.
They can generate synthetic financial data for back-testing trading strategies, risk assessment, market simulations, and more.
GANs have advantages such as autonomous training, data augmentation, and fraud detection, but they also have disadvantages like complexity and the need for large datasets.
CNNs are generally less complex than GANs and find applications in natural language processing and medical imaging.

Generative Adversarial Network In Finance Explained

The Generalized Adversarial Network Model consists of two networks: the generator and the discriminator. The generator creates identical data by using the distribution of accurate data as a model, tricking the discriminator into believing it is the actual input. A classifier called a discriminator is used to discern between genuine and fake data (this also involves setting GANs loss function, among other things). Both networks are constantly refining themselves to become more capable and to learn from each other's shortcomings.

Establishing Nash equilibrium—a stable system state incorporating participant interaction—between the two participants is the goal of the optimization process. GAN tries to accomplish is this equilibrium, which guarantees that no party may profit without changing the other's approach.

GANs are generative models that learn to generate plausible data through a competitive process where generator and discriminator networks are trained simultaneously. Due to their rapid growth and applications in various fields, accurate investigation of these networks is crucial.

Generative adversarial network applications are used in finance to generate synthetic financial data for back-testing trading strategies, risk assessment, market simulations or stock prices, and portfolio optimization. They can detect fraudulent activities by mimicking fraudulent patterns in synthetic data. GANs can generate synthetic economic indicators to simulate economic scenarios and assess risks. They can also generate realistic customer or consumer data that can be used for privacy protection and data regulations compliance. GANs can also generate synthetic financial time series data for predicting market trends, estimating asset prices, and simulating market conditions.

Examples

Let us consider the following instances to understand the topic even better:

Example #1

Suppose Danny, a financial analyst, analyzes stock market data and makes well-thought-out investment recommendations by using GANs. They can produce realistic simulations and identify intricate patterns, and hence, GANs are handy tools in the finance industry. Danny's GAN learns the underlying distribution of stock price fluctuations by training a GAN model on historical stock data.

It enables Danny to assess different investment strategies and produce simulated stock price trajectories. Danny can learn more about the possible risks and returns connected with various financial selections by contrasting the simulated results with actual market data. Danny gets assistance from the GAN in predicting future market patterns and locating possible profitable investment possibilities.

Example #2

Let's look at a study, through examination of counterfeit currency, to understand the actual world application of the concept.

DeepMoney is a machine-assisted method that uses GANs to differentiate real notes from fake ones. GANs train models that can make supervised predictions by using unsupervised learning. Modern image processing and feature identification techniques were employed to construct the entire strategy, which was then applied to Pakistani banknotes. The 80% accuracy rate of the high-precision machine was designed to identify paper money. Hence, the use of GAN was a proven success in the identification of fake notes.

Advantages And Disadvantages

Some of the advantages and disadvantages of GAN are given as follows:

Advantages:

Generative adversarial networks deep learning continues to train itself by generating training data and hence operates with little supervision.
GAN can efficiently produce particular collections of data, potentially replacing hours of human labor.
GAN improves data quality by comparing and correcting data instances.
GAN can lower labor costs by automating tasks that would otherwise require human effort.
Data augmentation and data shortage difficulties can be resolved with the use of GANs, which can produce synthetic data that is incredibly realistic and diversified.
By training on typical data and spotting deviations, GANs can be utilized for fraud and anomaly detection.

Disadvantages:

It requires technical knowledge and advanced data sets, hence the complexity of creating GAN systems.
Evaluating GAN results can be challenging due to task complexity or subjectivity.
GANs often require large data sets for accurate training, and more data can lead to accurate results.

Generative Adversarial Network vs Convolutional Neural Network

Some of the differences between the two concepts are given as follows:

Basis	Generative Adversarial Network	Convolutional Neural Network
Concept	A GAN is a model that consists of a generator and a discriminator engaged in a competitive process.	Convolutional Neural Network or CNN is a deep learning algorithm that processes input images, assigning importance (learnable weights and biases) to different aspects or objects in an image, enabling differentiation between them.
Purpose	GANs are utilized to generate new data that adheres to a specific pattern, such as realistic texts and images.	CNNs are employed to identify patterns in data, including object recognition in images or word recognition in text.
Complexity	GANs are more complex due to their complex architectures and being trained on larger datasets.	CNNs are generally less complex compared to GANs. CNNs have simpler architectures and are typically trained on smaller datasets.
Applications	Generative adversarial networks applications are present in varied fields, such as image, text, and music generation.	CNNs find applications in various domains, including natural language processing and medical imaging.

Frequently Asked Questions (FAQs)

1. How did generative adversarial networks make realistic-seeming images?

GANs generate realistic-looking images by training a generator network to produce images similar to the training data while simultaneously training a discriminator network to differentiate between real and generated images. This competitive process leads to the generation of increasingly realistic images.

2. Who invented GAN?

GANs were invented by Ian Goodfellow and his colleagues in 2014. Goodfellow, a renowned machine learning researcher, introduced GANs as a novel deep learning framework that involves training a generator and a discriminator simultaneously through an adversarial process. The invention of GANs has significantly impacted various fields, including finance, computer vision, and natural language processing.

3. Is GAN supervised or unsupervised?

GANs operate through unsupervised learning. In GANs, the generator and discriminator networks are trained simultaneously without labeled training data. The generator generates data, and the discriminator distinguishes between real and generated data, fostering a competitive learning process. This unsupervised approach allows GANs to learn and generate realistic data without relying on explicit supervision.