Intro to self-supervised learning
Self-supervised learning is one of the most exciting and rapidly evolving fields in artificial intelligence research today. In self-supervised learning, AI models are able to learn useful representations from unlabeled data by exploiting structure within the data itself. This differs from supervised learning, where models learn from human-labeled data, and unsupervised learning, where models uncover patterns in data without external guidance.
The key idea behind self-supervised learning is that models can learn from the data itself by solving pretext or proxy tasks derived from the structure of the unlabeled data. For example, in images, models can learn by predicting if an image is rotated correctly or not. In natural language, models can predict masked out words based on the surrounding context. By learning to solve these proxy tasks, self-supervised models acquire representations that can be useful for downstream tasks.
Interest and hype around self-supervised learning has exploded recently with breakthrough results matching or exceeding supervised techniques in computer vision, natural language processing, drug discovery and other areas. Given the vast amount of unlabeled data available, self-supervised techniques that leverage this data have the potential to greatly advance AI.
In this comprehensive guide, we’ll dig deep into self-supervised learning - how it works, key benefits, common self-supervised tasks, breakthrough applications, current limitations and the promising future outlook for this field.
What is Self-Supervised Learning?
Self-supervised learning falls between unsupervised learning, where models learn patterns from unlabeled data without external guidance, and supervised learning, where models learn from labeled examples.
In self-supervised learning, models learn representations and features by solving pretext or proxy tasks derived from the structure of unlabeled data itself. Essentially, the data provides the “supervisory signal” rather than manual human-labeled data.
For example, in images, common pretext tasks include predicting image rotations, restoring corrupted images, predicting if two augmented views of an image match, and arranging shuffled patches of an image. In natural language, models can predict masked words based on surrounding context or reconstruct scrambled sentences.
By learning to solve these pretext tasks, self-supervised models learn generalized representations that can be useful for downstream tasks. The key idea is that the pretext tasks require the model to understand important characteristics of the data, which leads to learning useful features as a byproduct.
Crucially, self-supervised learning allows models to take advantage of vast amounts of unlabeled data that may be infeasible to manually label. The learned representations often transfer well to downstream supervised tasks, reducing reliance on large labeled datasets. Self-supervised learning research also helps uncover the most important characteristics of data for feature learning.
Key Benefits of Self-Supervised Learning
There are several key potential benefits of using self-supervised learning:
- Reduced Reliance on Labeled Data
By leveraging abundant unlabeled data, self-supervised models reduce the amount of manually labeled data needed. This helps where labeled data is scarce or expensive to obtain. - Improved Data Efficiency
Related to the above, self-supervised pretraining improves sample efficiency. Models can learn high-quality features from unlabeled data before fine-tuning on a small labeled dataset. - Better Generalization
Since models learn more robust features from varied data, self-supervised learning can improve generalization compared to supervised-only models trained on limited labeled data. - Transfer Learning
The learned representations are often transferable to downstream tasks, allowing for pretraining on unlabeled data before task-specific fine-tuning. - Leveraging Unlabeled Data
Self-supervised learning allows leveraging of vast amounts of unlabeled data available, which supervised learning cannot capitalize on. Unlabeled data is abundant in many domains. - Combination With Supervised Learning
Self-supervised and supervised techniques can be combined, with unsupervised pretraining followed by supervised fine-tuning on downstream tasks.
Taken together, these benefits mean self-supervised learning can potentially advance AI by enabling more efficient use of data, less reliance on labeling effort, and improved model performance.
Common Self-Supervised Tasks
Many different types of pretext tasks for self-supervised learning have been explored, particularly in computer vision and natural language processing. Some of the most common categories include:
- Prediction Tasks
One popular approach is training models to predict parts of the input that have been masked or corrupted in some way. For images, models can predict cropped regions or missing patches. In text, predicting missing or masked words from surrounding context is common. - Context-Based Prediction
Related to the above, models can predict parts of the data from the surrounding context. For example, models can use caption context to predict missing words in images, or vice-versa predict image regions from caption text. - Contrastive Learning
Contrastive self-supervised learning trains models to distinguish between positive and negative sample pairs. The goal is to learn similarities within positive pairs and differences against negative examples. - Autoencoding
Autoencoders compress input data into a low-dimensional representation and then try to reconstruct the original input from this representation. The representation learned in the “bottleneck” encodes useful properties. - Exemplar Models
Some approaches learn visual concepts by identifying and clustering similar exemplars from unlabeled data. Relationships between the learned exemplar representations are then used for classification.
Numerous other pretext tasks have been proposed, including clustering, generation, and solving jigsaw puzzles with image patches. The choice of pretext task influences what representations the model learns. So this is an active area of research.
Applications and Breakthroughs
Self-supervised learning has recently driven breakthrough results in computer vision, natural language processing, and other areas. Some notable examples include:
- Computer Vision
Self-supervised models like Masked Autoencoders (MAE) have surpassed supervised models in image classification benchmarks. This demonstrates their ability to learn useful visual representations from unlabeled image data. - Natural Language Processing
Models like BERT produced large gains across NLP tasks through masked language modeling self-supervision. Performance on benchmarks like GLUE reached new state-of-the-art levels. - Robotics
Self-supervision from images and sensor data enables robots to autonomously learn useful skills like grasping objects, avoiding obstacles and navigating environments. - Drug Discovery
Self-supervised learning on molecular graphs and protein sequences is being applied to predict molecular properties and discover new drugs without expensive labeling.
The rapid pace of advances suggests that effectively tapping into unlabeled data with self-supervision can drive AI progress across many critical domains. Exciting new applications to fields like healthcare, materials science, and sustainability may be on the horizon.
The Future of Self-Supervised Learning
Given the impressive results so far, research interest and progress in self-supervised learning is likely to accelerate rapidly in coming years. Here are some promising directions for the future:
- More Efficient Models
Creating smaller, faster and more efficient self-supervised models would help adoption and impact, allowing application to new domains and tasks. - Combining Self-Supervision With Other Techniques
Integrating self-supervision with other learning paradigms like transfer learning, few-shot learning, and multi-task learning may yield further gains. - New Modalities and Domains
Applying self-supervision to data like audio, video, graphs, and multi-modal data could open up new frontiers for AI. Domains like biology, materials science, and sustainability are also promising. - Curriculum Learning
Dynamic curriculum learning, where models gradually tackle more difficult proxy tasks, could enable smoother learning and improved representations. - Reinforcement Learning
Combining self-supervision with reinforcement learning could allow agents to learn skills with minimal human input through environmental interactions.
Given the novelty of deep learning based self-supervised learning, we are likely seeing just the tip of the iceberg in terms of future capabilities and applications. Rapid innovation in this area may substantially advance artificial intelligence in coming years. The next wave of AI systems may be taught far more independently through self-supervision, requiring less human guidance.
Conclusion
In this guide, we have taken a comprehensive look at the growing field of self-supervised learning in artificial intelligence. Self-supervised learning allows models to learn useful representations from unlabeled data by solving proxy tasks derived from the structure of the data itself.
This technique has many compelling benefits - the ability to leverage abundant unlabeled data, reduced reliance on manual labeling effort, improved generalization from robust features, and stronger transfer learning abilities.
Significant recent advances demonstrate the promise of self-supervision across computer vision, natural language processing, drug discovery and other domains. Exciting future directions involve improvements in transfer learning, new modalities like video and audio, integrating self-supervision with other learning paradigms, and reduced model size and compute requirements.
While still a nascent field requiring much more research, self-supervised learning offers the potential to greatly expand the capabilities of AI systems by tapping into the vast amounts of unlabeled data available. If models can learn high-quality representations by themselves, it reduces the need for manually labeling every dataset. The representations learned also often transfer better compared to purely supervised approaches.
The coming years are likely to see self-supervised learning power advances across many critical domains like healthcare, science, sustainability and more. By enabling AI models to learn from the world with less explicit human guidance, self-supervision promises to unlock new levels of artificial intelligence that truly leverages the data all around us.
