Examples of Generative Adversarial Networks (GANs) uses
Image Compositing:Generate new, realistic images from a given distribution of data, such as human faces, landscapes, or animals.
Text-to-image compositing: Generate images from text descriptions, such as scene descriptions, object descriptions, or attributes.
Image-to-image conversion: Convert images from one domain to another, such as converting grayscale images to color, changing the seasons of a scene, or converting sketches to first-class photorealistic images.
Anomaly detection: Identify anomalies or outliers in data, such as detecting fraud in financial transactions, detecting network intrusions, or identifying medical conditions in medical imaging.
Data enrichment: Increase the size and diversity of your dataset for training deep learning models such as computer vision, speech recognition, or natural language processing.
Compositing: Generate new, realistic sequences from a given distribution of data, such as human action sequences, animal behaviors, or animation sequences.
Synthesis: Generate new originals from a given distribution of data, such as genres, styles, or instruments.
3D Model Compositing:Generate new, photorealistic 3D models from a given distribution of data, such as objects, scenes, or shapes.
Generative Adversarial Networks (GANs) are most popular for generating images from a given image dataset, but beyond that, GANs are now being used in a variety of applications. This is a type of neural network that has a discriminator block and a generator block that work together to be able to generate new samples in addition to classifying or ** the sample class.
Some of the newly discovered use cases for GANs include:
Security: AI has proven to be a boon for many industries, but it also faces the problem of cyber threats. GANs have proven to be of great help in dealing with adversarial attacks. Adversarial attacks use a variety of techniques to trick deep learning architectures. By creating fake examples and training models to identify them, we can combat these attacks.
Generate data using GANs: Data is the most important key to any deep learning algorithm. In general, the more data, the better the performance of any deep learning algorithm. However, in many cases, such as health diagnostics, the amount of data is limited, and in this case, high-quality data needs to be generated. Which GANs are being used.
Privacy protection: In many cases, our data needs to be kept confidential. This is especially useful in defense and military applications. We have a number of data encryption schemes, but each has its own limitations, in which case GANs can be useful. More recently, in 2016, Google opened a new research path for solving cryptographic problems using the GANs competition framework, where two networks must compete in creating and cracking.
Data manipulation: We can use GANs for pseudo-style transfers, i.e. modifying a part of a theme without the need for a complete style transfer. For example, in many applications, we want to add a smile to the image, or only work on the eye part of the image. This can also be extended to other areas such as natural language processing, speech processing, etc. For example, we can work on some selected words in a paragraph without modifying the entire paragraph.
Generative Adversarial Networks (GANssUses:Advantages:
Image compositing: GANs can produce high-quality, photorealistic images that can be used in a variety of applications, such as entertainment, art, or marketing.
Text-to-image compositing: GANs can generate images from text descriptions, which is useful for generating illustrations, animations, or virtual environments.
Image-to-image conversion: GANS can convert images from one domain to another, which can be used for coloring, style conversion, or data enhancement.
Anomaly detection: GANs can identify anomalies or outliers in your data, which can be useful for detecting fraud, network intrusions, or medical conditions.
Data augmentation: GANs can increase the size and diversity of the dataset used to train a deep learning model, improving its performance, robustness, or generalization.
Compositing: Gans can generate high-quality, photorealistic sequences that can be used in animation, movies, or games.
Synthesis: GANs can generate new originals that can be used for creation, performance, or entertainment.
3D model compositing: GANs can generate high-quality, photorealistic 3D models that can be used in architecture, design, or engineering.
Generative Adversarial Networks (GANssUses:Cons:
Training difficulty: GANs can be difficult to train and require a lot of computing resources, which can be a hindrance for some applications.
Overfitting: GANs may overfit the training data, resulting in synthetic data that is too similar to the training data and lacks diversity.
Biases and fairness: GANs can reflect bias and unfairness in training data, leading to discriminatory or biased synthetic data.
Explainability and accountability: GANs can be opaque and difficult to interpret or interpret, making it difficult to ensure accountability, transparency, or fairness in their applications.
Quality control: If generators and discriminators are not properly trained, GANs may generate unrealistic or irrelevant synthetic data, which may affect the quality of the results.