On Thursdays, we do deep dives into one of the freshest research papers or technology frameworks that is worth your attention. Our goal is to keep you up to date with new developments in AI and introduce to you the platforms that deal with the ML challenges.

💥 Deep Dive: Masterful AI, the Training Platform for Automated Computer Vision  

Machine learning is revolutionizing the way we do business and interact with technology. A key part of this revolution is computer vision (CV), which allows machines to "see" and interpret the world around them, providing a wealth of new opportunities for businesses and organizations. 

Facial recognition, object detection, and image classification are just a few of the many applications of computer vision. However, building models that can accurately recognize and classify images is not a trivial task. It requires a lot of data – often more data than is available – and extensive expertise in both data engineering and CV. 

Among the new startups tackling this challenge, Masterful AI stands out with a platform that makes it easy to automatically build CV models. But how does Masterful AI work? Let's take a closer look. 

Using Unlabeled Data 

One of the biggest challenges in training CV models is the lack of labeled data. Labeling data is time-consuming and expensive, so it's often not practical to label enough data to train a high-quality model. For example, a dataset of MRI images can take a radiologist days or even weeks to label. In some cases, it may not be possible to get all the needed labels for the data. 

Masterful AI addresses this challenge by making it possible to use unlabeled data for training. With its semi-supervised learning (SSL) capabilities, you can train your models without labels. This means that you can use all of the data that you have available, not just the data that has been labeled. 

SSL is a powerful technique that can significantly improve your CV models. It can help you to achieve better performance with no additional labeled data. 

One popular SSL method is consistency regularization. This approach applies a consistency regularization term to the final loss function, encouraging the model’s prediction to be similar in the vicinity of observed training examples. 

Another popular SSL method is pseudo-labeling. This approach adds a separate model’s predictions on unlabeled data to the training data. Further, graph-based methods are often used in SSL. This approach builds a graph of the data points and then uses this graph to infer label information for unlabeled examples. 

Finally, generative methods, like GANs, can also be used for SSL. These methods can either generate new data points that are similar to the ones in the training set, or a model can be trained to predict missing parts of an image.  

Augmented Data 

Another challenge in training CV models is the limited amount of data that is available. This is often due to the fact that collecting and labeling data can be expensive and time-consuming. 

In particular, in industries like healthcare and finance, data is often sensitive and confidential. This means that collecting and labeling data can require extra precautions to ensure privacy. As a result, there is often less data available for training CV models. 

This limited amount of data can lead to overfitting, where the model performs well on the training data but does not generalize well to new data.  

Augmented data is one solution to the challenge of limited data. Augmented data is data that has been generated by artificially adding noise or perturbations to existing data. This can help to increase the amount of data available for training without having to label more data. 

Creating useful augmented data is a challenge because different datasets benefit from different augmentations. To address this challenge, Masterful AI simplifies the usage of augmented data for training by automatically determining the optimal augmentations for a dataset. 

Training 

Once you have your data, you need to train your models. This process can be time-consuming and difficult to optimize. Masterful AI automatically finds optimal training hyperparameters like learning rate and batch size to automate this process, saving you time and effort. 

Five Principles of Deep Learning 

Masterful AI is based on five core organizational principles in deep learning: 

1. Architecture: The weights, biases, and activations that define a model. 

1. Data: The input used to train the model. This can include labeled, unlabeled, and synthetic data. 

1. Optimization: Finding the best weights for a model and training data. 

1. Regularization: Helping a model generalize to data it has not yet seen. 

1. Semi-Supervision: Training a model using both labeled and unlabeled data.  

These five principles are the foundation of Masterful AI's approach to automated computer vision. By making it easy to use all of these techniques, Masterful AI makes it possible to train high-quality CV models without the need for extensive data engineering and CV expertise. 

Three Objectives 

All the above address Masterful AI's three-headed approach to AutoML: 

1. Maximize accuracy with semi-supervised learning. 

2. Minimize manual experimentation by automatically testing and tuning the training loop. 

3. Minimize computing resources by using efficient algorithms and data structures. 

Using Masterful 

Like many ML tools, Masterful AI’s API is available as a Python package installable using the pip package manager. The current release is built for Tensorflow 2 and Tensorflow Object Detection API models.  

In the plans to add a command-line interface based on configuration files, to allow developers to train models without editing any code at all.  

Conclusion 

CV is a key part of the machine learning revolution. However, training high-quality CV models is not a trivial task. It requires a lot of data and expertise in both data engineering and CV. 

Masterful AI solves these challenges by making it easy to use unlabeled and augmented data for training. If you're looking for a platform to help you train high-quality CV models, Masterful AI is a good option to try.