Generative vs. Discriminative Models: Decoding Deep Learning

With rise in deep learning, newer algorithms like generative and discriminative models have become the talk of the market.While generative and discriminative models are being integrated with various application domains, the latent mathematical value of these machine learning techniques can drastically transform your product generation lifecycle. As a machine learning expert the major root anomalies are related to incompatible database management, insufficient storage, and mislabeling leading to failovers and errors.

Exploring generative and discriminative models and understanding their applications in deep learning technology  would build a learning curve and help you make an informed decision on which to choose for your private and sensitive data. Evaluating these options within data labeling software can be a  quick way to manage large volume, optimize storage and build robust predictive modeling processes.

Let's learn about these two predominant machine learning techniques and explore the nuances of both. 

As generative AI spurts even further, newer ways of data predictions are in full experimentation across industries. Generative and discriminative models are in a league of their own, but follow different data manipulation and analysis methods to predict outcomes.

How generative and discriminative models work

When algorithms are given large amounts of data to train a generative model, it’s used to help the algorithm identify structures and patterns that will help create new outputs. The generative model learns the probability distribution of these patterns and then makes new outputs that resemble the original dataset. Even if data is unlabeled, generative models can still distinguish the patterns in the data and create similar outputs.

For discriminative models, unlabeled data is much more of a challenge. Discriminative models need labels to understand where the boundaries are between data types, classes, or categories. For example, an image showing a dog, a cat, a ball, and a tree needs to have labels on each of these elements for the model to distinguish the boundaries of these objects. These models are easier to create than generative models because they can work effectively with smaller amounts of training data and simple boundary labeling. 

Formula for generative model

Generative model focuses on learning from past model behaviour and repurpose that to predict newer categories for newer data points. Mostly used in unsupervised learning, it is very vital for sentiment analysis, anomaly detection, spam detection and noise reduction.

Formula for discriminative model

Discriminative model only focuses on the decision boundaries to assign fast labels to datasets using a "fission" technique. However, discriminative model sometimes need to be double checked for "misclassification"

As it is evident from the formula, generative model use the binominal data distribution to derive context and patterns within the data itself, whereas discriminative model calculates the class probability fast with mind mapping and past classification.

Generative model types

Whether you’re looking to create a new output entirely or analyze existing data determines which type of model you use. 

Generative models are better suited for applications that require a new output. The most common examples are described here.

• Bayesian networks, also known as a Bayes’ Network, use directed acyclic graphs (DAG) to calculate probabilities or detect anomalies in data. They draw Bayesian inferences, a type of statistical prediction that updates the probability of a hypothesis as more information becomes available. As the generative model creates new outputs based on the training data, this new data is then fed back into the algorithm to continue assessing patterns. This allows the model to create more likely probabilities as more data is reviewed.
• Autoregression generative models are primarily used for time series modeling, where correlations between past behaviors in the data are used to predict future behavior. Autoregression is particularly helpful in a number of industries, with applications like sales figure predictions or investment strategizing.
• Generative adversarial networks (GANs) use both generative and discriminative modeling as part of the wider generative model. The generator initially trains and produces new data points over time. These outputs are then fed into the discriminative submodel to classify which parts of the generated data are real or fake.
• Naive Bayes: Naive Bayes is a simple probabilistic method that classifies new input data by comparing it's features or independent attributes to previous probability distribution. It is entirely based on "Bayes theorem" and is a popular method to classify smaller datasets. 
• Markov Random Field: Markov random field represent the joint probability of variables using a technical graphs where variables are nodes and edges can hint the dependencies within data. It is a common algorithm used for natural language processing and large language modeling.
• hidden Markov model: A hidden Markov model is a statistical algorithm to interpret and classify sequential data where the output depends on hidden states. It captures the right class via hidden state and activation function that determines the right class. 
• Latent Dirichlet Allocation : This generative model is used for topic modeling. It assumes documents as a mixture of topics, generates probabilities for different topics and assigns probabilities to each topic before assigning it to a specific category.

Discriminative model types

Discriminative models are more appropriate for analyzing existing data than creating a new output. They use this information to determine boundaries between categories or objects in text and images. This allows users to identify both patterns and anomalies in large data sets.

For instance, information entered into a discriminative model can separate college grade data into Pass or Fail categories based on previous data labeling. The most common types are defined here.

• Logistic regression is a simple linear model used for binary classification between two distinct groups in the data. The values of the input data must result in an output between 0 and 1. For example, banks could use logistic regression to predict whether a card transaction was genuine (0) or fraud (1) – there are only two possible outcomes in the data analysis, and nothing new is being created. The model is simply assessing relationships between the input data points.
• Decision trees are also used for classification work. These models use an “if this, then that” structure to create branches of possible outcomes based on certain choices. The tree progressively splits data into smaller and smaller groups based on the attributes of each individual data point. The tree continues to branch out, with fewer pieces of data in each branch as the tree goes on until data can no longer be further divided.
• Support vector machines (SVM) can be applied to both classification and regression work. The boundaries of two data points create an empty space between them known as the support vector or margin. This is like a buffer zone between two objects or data points, so the larger the margin, the better the model is at identifying these as two separate classes. SVMs have numerous applications, from facial recognition software to sentiment analysis.
• Neural networks (for classification):  Neural networks are mostly process sequential text by storing input in a hidden state and triggering the right output node in the next state in accordance with the previous input. The most popular type of neural net is artificial neural network (ANN), RNNs and CNNs. 

Discriminative models are largely used for image classification and object detection in machine learning, as they use large scale neural networks that mimic human understanding to identify the qualities of an image. They can also be used for natural language processing (NLP) tasks like sentiment analysis and multilingual translation reviews.

Industries that use generative models

With generative models creating new outputs, industries that already have a large amount of data can use them in a variety of ways to make their work more productive and efficient.

Healthcare

The medical field has to overcome numerous challenges in order to save the lives of patients. Predictive and generative artificial intelligence (AI) tools help medical professionals report on imaging, discover new medication through synthesized research, and personalize treatment for patients based on their needs and medical history. 

Generative models can also give doctors and pharmacists more time in their day by simplifying and automating tasks like transcribing patient notes and summarizing patient information for review.

Marketing 

In the advertising world, marketers use generative models to create campaigns that reach new markets. Tasks like writing product descriptions or creating search engine optimized (SEO) image tags take up significant time, but generative AI tools can expedite this process and leave teams able to spend more time on strategy development. AI models can also create personalized recommendations for customers based on the data about their previous experiences.

Manufacturing 

Engineers and manufacturers can accelerate their design process by using generative AI tools to create new ideas that fit within a project’s constraints and match similar projects completed in the past. 

Generative models can also be used to track ongoing maintenance needs for equipment based on historical data and alert teams to potential issues before a machine malfunctions.

Media 

New media, like visual and audio content, can be produced using generative tools. Sports or live event companies can easily make highlight reels with generative AI to give fans information faster than when humans alone do the work.

For news outlets and online publications that manage thousands of pieces of data and content, generative AI can make locating and updating existing files much easier.

Industries that use discriminative models 

Although discriminative models don’t create new data themselves, they still have plenty of uses. Many industries benefit from these tools to improve company decision-making and enhance their business performance.

Retail and e-commerce

When you’re a salesperson, you have to understand market trends and the potential impact they have on future sales, whether that’s online or in a brick-and-mortar store. Using discriminative models, business owners can take previous historical data and make more accurate predictions about what revenue will look like.

Discriminative models simplify proactive decisions that affect your business’s bottom line. You can better optimize your product strategies, like predicting seasonality and product popularity, as well as gain a better understanding of customer behavior.

Advertising and marketing 

Selling products and services requires an understanding of how customers think and behave. Using existing data in a discriminative model, marketers can segment customers based on patterns in their behavior and create targeted campaigns around this segmentation. 

For instance, customers who fit into the parent category could receive targeted marketing for back-to-school sales, increasing the likelihood that they’ll make a purchase because the advertising speaks to their circumstances.

Finance 

Even though finance is still seen as an old-fashioned industry, its inner workings are rapidly changing to catch up with technology, including using discriminative AI models. Financiers have made predictive modeling popular, primarily when it’s focused on making forecasts about stocks or interest rates using past data and economic reports.

Financial professionals also rely on predictive analysis to examine translation data to find anomalies that may point to fraudulent activity or determine risk levels.

Autonomous vehicles and machinery 

Discriminative modeling is particularly useful in self-driving, or autonomous, vehicles because it can identify object boundaries in image classification and labeling. Understanding objects in the real world, in real time, keeps riders and pedestrians safe and lets autonomous vehicles accurately map the world around them with image-based predictive modeling.

Generative vs. discriminative model: benefits and challenges

Generative AI remains in its infancy so users shouldn’t expect too much from these tools initially. If accuracy is a priority, discriminative models will suit you better. But other types of AI models come with significant challenges.

Accuracy and reliability 

The newness of generative models means they often create inaccurate data when issues in the dataset exist. We shouldn’t ever fully rely on these tools because of the potential for inaccuracy that unlabeled and unsupervised data have. Discriminative models have their own flaws but are typically more reliable and accurate since they only use labeled data.

Privacy 

Privacy in AI modeling can be a problem with both types, but especially for generative. If private or sensitive content within training sets is used to create new outputs, the security of the original data might be compromised. 

Bias and transparency 

Bias presents an especially difficult challenge for generative AI. Whenever training sets have bias or lack transparency about how the algorithm has been built, the outputs created by these models will inherit the implicit biases from the data they’ve been trained on. Building policies to control for this as much as possible is essential when creating a new generative model.

First label; then qualify

While generative models offer functionalities to predict common trends and classify future tokens, discriminative models bucket data smartly by creating mind maps. Both these techniques are being adopted to process larger sequential datasets and create ripples across industries like marketing, healthcare, banking or retail. These algorithms will improve in performance and can be deployed over wider industrial sectors to achieve higher degrees of accuracy.

Explore how recurrent neural networks are used to process sequential data and generate coherent responses for textual queries.