- Saliency detection
- Scalable machine learning
- Self-organizing maps
- Self-supervised learning
- Semantic segmentation
- Semi-supervised clustering
- Semi-supervised learning
- Sentiment analysis
- Sequence labeling
- Sequence-to-sequence learning
- Siamese neural networks
- Simulated annealing
- Social network analysis with AI
- Sparse coding
- Speech recognition
- Speech synthesis
- Spiking neural networks
- Statistical machine learning
- Statistical relational learning
- Stochastic gradient descent
- Stochastic optimization
- Streaming learning
- Structured prediction
- Style transfer
- StyleGAN
- Support vector machines
- Swarm intelligence
- Swarm robotics
- Synthetic data for privacy-preserving AI
- Synthetic data generation
What is Self-supervised learning
Self-Supervised Learning: A Comprehensive Guide
Self-supervised learning is a type of machine learning that eliminates the need for labeled data by training models on the available dataset and using the information present in the input data itself. In self-supervised learning, the model uses an auxiliary task to utilize the data's inherent structure and make predictions based on these structures. This technique is becoming increasingly popular in the present time with the tremendous increase of data and lack of labeled data for many applications.
In this article, we will discuss self-supervised learning, how it works, and its applications in various domains.
How does self-supervised learning work?
In self-supervised learning, the model predicts certain aspects of the input data, and the predicted values' correctness is verified through some loss function. This loss function is called the pretext task and is used to train the model without requiring any labeled data. After the model is trained on the pretext task, the model's weights are used to solve the target task, which is a supervised learning problem.
The pretext task is designed to use the natural structure of the data being analyzed, such as predicting missing pixels in an image or predicting the neighboring words in a sentence. As the pretext task is relatively easy to solve but still requires a high-level comprehension of the data, the model learns to extract crucial features from the data, which are used for the target task, often with excellent results.
Usually, self-supervised learning is used in three ways:
- Pre-training: The model is trained on a pretext task and then fine-tuned on a supervised task. Pre-training is used to improve the model's generalizability, especially when there is significantly less labeled data available.
- Co-training: The model is trained on multiple tasks to combine the knowledge learned from them. Co-training can bring better-domain-specific features as it incorporates the inherent structure of the data.
- Multi-task learning: The model is trained on a supervised and unsupervised task simultaneously. This is used when there is a lack of labeled data or the cost of acquiring labeled data is more than unsupervised data.
Applications of Self-Supervised learning:
There are several applications of self-supervised learning that have been tried out successfully with good results. Here are a few examples:
- Computer Vision: Self-supervised learning has many applications in computer vision, such as image recognition, object detection, and image segmentation. Self-supervised Networks such as SimCLR has been shown to surpass supervised approaches like ResNet in accuracy in certain image classification datasets.
- Natural Language Processing(NLP): Self-supervised learning has been useful for unsupervised pre-training of neural network architectures used in NLP domain such as the transformer models. The BERT model, which relies on language modeling and is pre-trained on unsupervised data through masked language and next sentence prediction tasks, is an excellent example of self-supervised learning in NLP.
- Speech Recognition: Self-supervised learning has been used for unsupervised learning of speech representations, which have been shown to perform well on tasks such as audio sentiment classification and speaker verification.
Pros and Cons of Self-Supervised learning:
Just like any other model or technique, self-supervised learning has its advantages and disadvantages. Here are some advantages:
- Self-supervised learning eliminates the need for labeled data, which is often expensive and time-consuming to create.
- The technique can be used to create better-domain-specific features compared to supervised learning where features might not generalize well across different tasks.
- Pretraining with unsupervised techniques like self-supervised learning can help improve deep learning models' generalization performance.
However, there are also some disadvantages of self-supervised learning:
- Training a self-supervised model can be computationally expensive and time-consuming as models need to be trained on large datasets to learn meaningful features.
- Appropriate pretext tasks need to be identified to train a successful self-supervised model. This can be challenging and time-consuming as it requires the understanding of the data's inherent structure.
- The self-supervised model's performance depends on the quality of the pretraining which is often quite sensitive to the hyperparameters.
Conclusion:
In conclusion, self-supervised learning is an innovative and exciting technique that has been shown to perform well on several applications. The technique of learning features from unlabeled data without human supervision can drastically reduce the cost and time of acquiring labeled data and enhance the generalization of supervised models. While self-supervised learning brings several advantages, one should not overlook the possible computational overhead and sensitivity to hyperparameters.