# A Beginner’s Guide to Self-Supervised Learning > Source: > Published: 2026-08-17 02:28:51+00:00 The advancements in Artificial Intelligence (AI) have opened so many doors in numerous domains. Over the past few years, we have witnessed how the world has changed with a rapid digital revolution. This eventually impacted the data and metadata. In today’s world, data is the new oil. The data is being generated every second at a speed faster than light. People quote that a Data Scientist is the hottest job in the current market. Despite these great achievements, researchers face several challenges every day in their labs with the data. However, the sophistications of the developments in AI brought ways to build great things out of almost nothing. One such amazing thing is Self-Supervised Learning (SSL), a technique used to build efficient models with almost no labeled data. In this blog, we’ll dive deep into the domain of SSL and see how it changes the field of AI. Self-supervised learning (SSL) is a machine learning technique which allows the model to extract the rich feature representations from the unlabeled data during the pre-training phase and later, it can be finetuned for the downstream tasks using very less labelled data. This is a combination of supervised (labeled data) and unsupervised learning (unlabeled data). Self-supervised tasks inherently has two different phases, which are as follows: This process is quite analogous to our formal education. Generally, we tend to learn diverse concepts till our high school. Once we enter into college, we pick a specific stream and try to master it. We do the same thing with self-supervised learning. During the initial phase, we train the model with huge amounts of unlabeled data without any specific goal. After the pre-training stage, we instruct the data to find the relationships with the provided labeled data and expect it to work in relation to the downstream task. Today, most of the large vision, language, and multimodal models heavily rely on SSL during the pre-training stages. Self-supervised learning is mainly used in the fields of Computer Vision (CV) and Natural Language Processing (NLP) where it is typically a large amount of labeled data is expected for training the models. However, in the real world, it is very rare to find a large amount of labeled data for specific use cases. Especially, when you are working in an under-explored domain, it becomes really hard to find even tens or hundreds of labeled data points, where you might be expected to have thousands of data points to train the model. Data collection and labeling is time-consuming and sometimes really expensive. At times, we might find it feasible to spend our time or money on it due to several reasons. Especially when you require a human expert to manually label the data points, it becomes a tedious process. The main goal of self-supervised learning is to minimize the need or dependency on labeled data. For many years, researchers have been struggling to find good labeled data for their use cases to train good performing models. However, this was really a draining process. SSL provides a way to work and build efficient models even with small amounts of data. This helps to minimize the lengthy lifecycle of projects into a shorter span of time. The SSL framework is commonly known as one step closer to embedding human cognition into machines. On a high-level view, this might sound quite similar to semi-supervised learning. But they are conceptually different from each other. Semi-supervised learning is a single task which requires both labeled and unlabeled data. However, SSL is a two-step process (independent tasks) which does not require human labels during the pre-training stage. The labels are derived from the structure of the unlabeled data. The primary goal of semi-supervised learning is to improve the performance, whereas in SSL, we try to learn the generalized representations from the available data. The most common techniques used in SSL tasks are categorized into four buckets. The core idea of contrastive learning is t**o pull similar (positive) examples closer and push dissimilar (negative) examples farther **in the representational or feature space. This can be done by defining a loss function called contrastive loss function. The work of this loss function is to maximize the similarity between the positive pairs and minimize it for the negative pairs. The positive pairs are typically created through data augmentations of the same instance, whereas the negative pairs come from different instances in the batch. The most commonly used loss function is **Noise-Contrastive Estimation (NCE)** or its variant, **InfoNCE**. The key algorithms involve, Contrastive learning allows producing high-quality, transferable representations that often outperform supervised baselines on downstream tasks like classification or detection. It also supports data augmentation and scalable to large datasets. However, it requires many negatives, which can be computationally expensive and might lead to bias from false negatives. It is quite sensitive to the choice of augmentations and hyperparameters like temperature. Non-contrastive learning helps to overcome the limitations of contrastive learning by eliminating the need for negative samples. Instead, it focuses on preventing the representation collapse through architectural tricks such as asymmetric networks, stop-gradients, or regularization techniques. The goal is to learn the general representations, without repulsion. The key difference from contrastive is that we **only use the positive pairs**, which reduces the overhead. Collapse is prevented by techniques such as momentum updating or predictor networks. The loss function used here is often a simple similarity maximization with anti-collapse mechanism. For example, mean squared error (MSE) or cosine similarity between predictions and targets. The common strategies for collapse prevention are discussed below. The key algorithms used in non-contrastive learning are as follows. Eventually these features make non-contrastive learning, a method for efficient computation, less sensitive and better performing. However, it can still collapse if the anti-collapsing mechanism fails, and it is less intuitive than the contrastive methods. Masked prediction is a technique, in which parts of the input are intentionally hidden adn train the model to reconstruct or predict the missing portions. This creates a pretext task that forces the model to understand the context and structure. The masking strategy can be random or patch-based. The loss function can be MSE for continuous data or Cross-entropy for discrete data. The loss function is only computed on the masked parts to focus on the prediction. It often uses the transfomers (encoder-decoder architecture) where the encoder processe visible inputs, and a light-weight decoder reconstructs. The key algorithms which uses masked prediction are listed below. Masked predicition is a simple and effective technique used for sequential or grid-structured data. It encourages to learn global context and semantics. However, It is computationally expensive and may focus on too much low-level details unless using tokenized representations. Also, it is less-effective for non-structured dat without adaptations. As the name defines, Cluster-based SSL integrates clustering into SSL pipeline to generate pseudo-labels or assignments. It alternates between clustering representations and refines the model based on the cluster consistency. The embeddings are clustered, and the model is trained to predict cluster assigments for augmented views. The loss function used here is cross-entropy on pseudo labels or entropy minimization to encourage confident assignments. Itis often combined with contrastive elements. The key algorithms of cluster-based SSL are discussed below. It handles large-scale data well, produces semantically meaningful groupings without labels, and robust to noisy data. Although it might sound good, it is still sensitive to hyperparameters and can be computationally expensive. Self-supervised learning is used in diverse domains to a greater extent. Some of the applications are listed below. Most real-world SSL systems used the combination of SSL techniques rather than any single one in isolation. The most dominant combination in the market is **contrastive pretraining + masked reconstruction + non-contrastive stabilization tricks.** In short, the answer is NO. SSL cannot and should not be used in all the use cases. So, how to decide when to use SSL techniques. Well, this decision depends on several factors. **2. General-purpose representation learning** **3. Noisy labels** **4. Foundation systems** In these cases, you can definitely try out the self-supervised learning. However, we cannot choose SSL under certain situations. In such cases, you can preferably go for supervised learning. **2. Highly task-specific outputs** **3. Compute or time constraint** **4. When label encode critical semantics** Think wise before you make a decision to use SSL for your use case. However, you can feel free to try out and explore multiple techniques. Though the field is still young, the techniques are still evolving rapidly. If you are someone wondering “may be I should try SSL” — the answer is almost certainly yes. We have great tools and an excellent open-source ecosystem. I’m an avid fan fo self-study. Throughout my life, I heavily relied on self-study. So, why not allow models to perform self-study, why still spoonfeed them? Let them learn on their own, without any annotations. I hope you would have enjoyed reading this blog. Do let me know your feedback in the comments section. I’m happy to hear from you. **Stay curious!** **Note:** The content of this blog is a curation of data from multiple sources. I have referred a lot of publicly available content and refined them into a single piece. [A Beginner’s Guide to Self-Supervised Learning](https://pub.towardsai.net/a-beginners-guide-to-self-supervised-learning-27941f2bb4a9) was originally published in [Towards AI](https://pub.towardsai.net) on Medium, where people are continuing the conversation by highlighting and responding to this story.