Supervised learning is the approach most machine learning developers use if they want to take control of their models. However, supervised learning unfortunately relies on a significant amount of quality, labeled data, and the cost to annotate all that information greatly slows down training efforts and increases the time-to-value.
To eliminate this bottleneck, new advancements in ML development are now focusing on self-supervised learning, which enables the algorithm to work with unstructured data by itself. With enough progress, this solution will soon allow ML to scale effectively at a low cost, skipping much of the tedium of gathering and labeling input data.
Self-Supervised Learning (SSL) is a machine learning technique that allows algorithms to process data more independently and reduces the need to label input data prior to training.
Understanding the Background Behind SSL
To understand how a self-learning algorithm works, we need to look at the types of learning involved in ML model development.
- Supervised learning, popular in neural networks, relies on labeled data. Developers provide the model with examples and dictate what prediction the model should return with the given example. Models in computer vision (specifically object classification) often use supervised learning.
- Unsupervised learning, used in deep learning techniques, requires the algorithm to infer predictions by finding patterns in the data. Unsupervised learning does not require developers to label the data beforehand.
- Semi-supervised learning is a mix of the two. A model receives both labeled and unlabeled input data. Think about how a math teacher would explain the solution to a problem to a student, who would then use that knowledge to solve a separate but similar problem.
- Reinforcement learning leverages a feedback system to reward correct predictions and discourage false ones. The same technique that works for training dogs also works for AI models.
By contrast, self-supervised learning occurs when a machine learning model trains itself on a set of input data and extrapolates its thinking to larger data sets. The approach is also known as predictive or self-labeling learning for this reason.
You can think of self-supervised learning as the ML model generating its own labels given what it knows from previous data points. A well-known example is natural language processing (NLP), which can predict the next few words in a sentence given a portion of it.
Self-Supervised vs. Unsupervised Learning
Self-supervised and unsupervised learning both negate the need to label the dataset. It’s possible to consider self-supervised learning as a subset of unsupervised learning. However, the two differ in their objectives.
Unsupervised learning greatly focuses on the model, while self-supervised focuses primarily on the data. Unsupervised also does not rely on feedback loops, while self-supervised essentially uses the existing dataset as a source of feedback.
How Self-Supervised Learning Works
Under this learning procedure, the algorithm obviously starts with an unlabeled dataset. It generates labels for that initial data first before undergoing pre-training, where the model trains itself using those labels. From there, developers fine-tune the model by applying it towards the intended tasks.
Self-supervised learning can come in several forms.
- Predictive algorithms attempt to understand the initial data and tailor its own labels. These implementations categorize data through clustering.
- Generative models look at the distribution of the data and attempt to predict how likely an example will occur, such as the next word in a sentence.
- Contrastive learning looks at the overall features of a dataset and attempts to determine whether two points are similar or different. A computer vision algorithm that can distinguish pictures of dogs from those of cats is one example.
When done correctly, self-supervised learning allows a machine learning implementation to improve its performance for downstream tasks by itself.
What Is the Appeal of Self-Training Machine Learning?
The advantages of a self-supervised learning algorithm are obvious. Other forms of learning suffer from a heavy reliance on human input during the training process. The need to gather, cleanse, and label datasets results in high monetary and time costs and lengthy development cycles that cut down on the value businesses would otherwise receive from machine learning.
Even many ML developers consider the process of cleaning, annotating, and organizing data to be an annoying and tedious task that’s become unfortunately a large part of their workdays. The push for self-training is strong, and progress has been promising.
Current Challenges Facing Self-Supervised Learning Implementations
Despite its potential, companies experimenting with self-supervised learning often run into setbacks.
The first issue with not using labeled datasets is the potential to lose model accuracy. SSL essentially trades labeled data for a vast amount of data, as sufficient information is necessary for the model to start accurately labeling its own sets. Incorrect inferences simply result in counterproductive learning.
Another challenge is computational capacity. While supervised learning only requires training for a labeled dataset, SSL requires multiple runs for the algorithm to generate its own labels and then train itself on those labels.
Finally, SSL rarely gives the algorithm enough information about the context of the dataset. For instance, a machine learning model that aims to remaster low-resolution images into clearer ones will probably pick up on compressed artifacts in the training set and attempt to mimic it in its own output. Some human intervention will likely be necessary to discourage the replication of defects.
Potential Applications of Self-Supervised Learning
The exciting potential of self-supervised learning lies in its many industrial applications, ranging from manufacturing to scientific studies.
For example, computer vision applications can train more quickly on unstructured, unlabeled data and pick up on the nuances of the input image sets. Self-supervised learning for object detection can effectively generate its own labels without introducing potential label bias common in manual methods.
Another example is natural language processing. Language models today that use self-supervised learning, such as BERT, can predict the next words in a sentence using prior context. BERT itself, Bidirectional Encoder Representations from Transformers, has found use at Google for determining the context behind search queries for more accurate search results.
We’re also seeing new applications in Next Sentence Prediction (NSP) algorithms and machine translation systems.