Generative AI Tutorial

A Generative Adversarial Network (GAN) is a type of artificial intelligence framework made up of two neural networks- a Generator and a Discriminator.

The generator generates new data samples that are intended to resemble real data from the dataset.

The discriminator evaluates the input data and tries to distinguish between real data samples from the dataset and fake data samples generated by the generator.

During the training of a GAN, both the generator and the discriminator are trained simultaneously but in adverse ways, i.e., in competition with each other.

Ian Goodfellow, et al. in the 2014 paper demonstrated how to use GANs to generate new examples for the MNIST handwritten digit dataset, the CIFAR-10 small object photograph dataset, and the Toronto Face Database.

Alec Radford, et al. in 2015 paper titled "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks" demonstrated DCGAN models for generating new examples of bedrooms.

Tero Karras, et al. in their 2017 paper demonstrated how to generate realistic photographs of human faces, objects, and scenes.

Yanghua Jin, et al. in their 2017 paper titled demonstrated how to train and use GAN to generate faces of anime characters.

Variational autoencoders (VAEs) are a class of generative models that generate parameters for probability distribution in the latent space and then decode it back. This feature enables VAEs to capture the underlying probability distribution of the input data samples and generation of diverse and realistic samples.

VAEs consists of two main components: an encoder and a decoder. Encoder maps the input data samples to the parameters of a probability distribution in the latent space. The decoder reconstructs the data samples by using the samples from latent space.

Some of the ethical considerations in Generative AI include the creation of harmful content and misinformation, copyright infringement, and data privacy violations. It also involves social biases and fairness issues, impacts on employment, lack of transparency, and challenges in regulatory compliance. Addressing these concerns is necessary to ensure responsible development and deployment of generative AI technologies.

Some of the common challenges of training Generative AI models include high computational costs, ensuring data quality and diversity, preventing mode collapse, balancing generation quality, scalability, conditioning information quality, and maintaining training stability.

Due to these challenges, developing robust and reliable generative models has become complicated. That’s why these issues require significant resources and expertise to address effectively.

Some of the popular tools and frameworks for working with Generative AI are as follows −

• TensorFlow
• PyTorch
• Keras
• OpenAI's GPT
• Hugging Face's Transformers
• GAN Lab
• DeepArt
• RunwayML
• NVIDIA StyleGAN
• Magenta

These tools provide libraries and pre-trained models for development, training, and deployment of generative AI applications across various domains.

Yes, generative AI models can be used for data augmentation. Generative model types such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) can create synthetic data to increase the size of a training dataset by creating modified versions of images in the dataset. When the original data is limited or imbalanced, data augmentation helps reduce overfitting, and enhance model performance.

Generative AI can be applied to text generation and natural language processing (NLP) by creating models that generate coherent and contextually accurate text. Some of the most common applications include −

• Chatbots and virtual assistants
• Text completion and auto-suggestion
• Machine translation
• Content creation
• Text summarization
• Sentiment analysis
• Personalized content
• Information retrieval
• Question-answering and Education tools

To get started with building your own Generative AI models, follow the below given steps −

• Learn the Basics − First, understand the basics of Machine Learning and Deep Learning.
• Choose a Framework − Select a deep learning framework like TensorFlow or PyTorch.
• Knowledge of Generative Models − Study some generative models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
• Set up the Environment − You need to set up your environment using Python libraries.
• Collect and Prepare Data − Now, collect a dataset relevant to your task and preprocess it for training.
• Train the Model − Using the preprocessed data, train your model.
• Evaluate Performance − Evaluate model’s performance using appropriate metrics. appropriate for your task. You can make improvements by adjusting its parameters.
• Use Pre-trained Models − You can also use some pre-trained models for better efficiency.

Conditional generative AI models, as the name implies, generate output based on some specific condition information like class labels, attributes, or even other data samples. For example, Conditional Generative Adversarial Networks (CGANs), where the output generation can be controlled by labels like "cat" or "dog".

Unconditional generative AI models, on the other hand, generate output without any specific condition or labels. These kinds of models learn to produce data that mimics the overall distribution of the training dataset. For example, standard GANs and VAEs are unconditional in nature.

Generative AI models learn to mimic the style of input data through a process called training, which involves several steps −

• Collect and Prepare Data − Collect a dataset relevant to your task and preprocess it for training.
• Model Initialization: Initialize a generative model. For example, either a GAN or VAE.
• Training − The generative model learns by iteratively adjusting its parameters to minimize the difference between generated output data and real data. Training involves Loss Functions that measure how close the generated data is to the real data and Optimization to minimize the loss.
• Feedback Loops − With the help of feedback loop in training process, generative AI model improves. For example, in GANs, there is a feedback loop between the generator and discriminator networks.
• Feature Learning − The generative AI model captures underlying patterns and features in the data that allows it to generate new data identical to input data.

Below are some of the techniques for controlling the diversity of generated outputs in generative AI models −

• Temperature Sampling
• Latent Space Manipulation
• Conditional Generation
• Noise Injection
• Data Augmentation
• Multiple Sampling Passes
• Regularization Techniques

We can use below given strategies to prevent generative AI models from generating biased or offensive content −

• Clean Training Data − Use training data that is diverse and free from biases and offensive material.
• Bias Detection and Mitigation − We can use techniques like fairness constraints and bias correction algorithms to detect and mitigate biases during and after training.
• Content Filtering − In generated outputs, we can implement filters to detect and remove offensive content.
• Ethical Guidelines − Follow ethical guidelines for the development and deployment of models.

The computational requirements for training and deploying Generative AI models depend upon the factors like model complexity, dataset size, and hardware resources. For example, training processes require high computational power such as GPUs or TPUs, and large storage for datasets. On the other hand, deploying models for real-time applications may require optimized architectures and efficient inference algorithms.

Generative AI models learn from examples to generate new data, while traditional rule-based systems follow some predefined set of rules. Generative AI models require extensive training on large datasets to adapt to different situations whereas traditional rule-based systems are simpler and use fixed rules.

Generative models can generate new output data based on what they learned, while rule-based systems follow strict instructions. Both have strengths and weaknesses, like generative AI models are flexible but difficult to understand, and rule-based systems are easy to understand but less adaptable.

Current Generative AI models have several limitations −

• Data Dependency − Generative models depend on large and diverse dataset for training.
• Mode Collapse − Generative models like GANs suffer from mode collapse where they learn to produce limited varieties of samples.
• Ethical Concerns − The Generative models can be misused for creating deepfakes, spreading misinformation, generating biased or offensive content.
• Training Instability − Models like GANs suffer from training instability. To avoid this, models require careful architecture design and training approaches.
• Computational Resources − Training and deploying generative models require high computational power which makes them inaccessible to some applications.

Scaling Generative AI models to handle large datasets faces several challenges −

• Data Management − Handling large datasets may face challenges in terms of storage, access, and preprocessing.
• Model Complexity − It may increase model complexity i.e.; the model requires longer training times and higher computational costs.
• Overfitting − Scaling to handle large datasets involves a risk of overfitting in which the model learns the training data instead of learning generalizable patterns.
• Data Distribution − With scaling, it will become challenging for models to capture all aspects effectively.
• Hyperparameter Tuning − Optimizing hyperparameters becomes more complex with larger datasets.