Amazing technological breakthrough possible @S-Logix

Office Address

  • #5, First Floor, 4th Street Dr. Subbarayan Nagar Kodambakkam, Chennai-600 024 Landmark : Samiyar Madam
  • +91- 81240 01111

Social List

Research Topics for Semi-supervised Learning

Research Topics for Semi-supervised Learning

Masters and PhD Research Topics for Semi-supervised Learning

The semi-supervised learning algorithm is one of the machine learning models, learning the patterns from labeled and unlabeled data to perform certain learning tasks. The main goal of semi-supervised learning is to address the constraints in both supervised and unsupervised learning models. A Semi-supervised learning algorithm is a stable algorithm that builds a learning model with a small number of labeled patterns and many unlabeled patterns in the input samples.

Examples of Semi-supervised Learning

There is no way for the data to be labeled promptly because the volume of data is always increasing by leaps and bounds. Consider a frequent TikTok user who uploads up to 30 videos daily. Moreover, there are 1.5 billion active users. In such a situation, semi-supervised learning can brag of a wide range of use cases, including classification of text documents, web material, and speech recognition.

Speech Recognition: Labeling audio requires a lot of time and resources; semi-supervised learning can overcome these obstacles and deliver superior results. Facebook (Meta) has successfully enhanced its voice recognition models using semi-supervised learning, namely the self-training method. They began with the base model, developed using 100 hours of audio data annotated by humans. The performance of the models was then improved via self-training and the addition of 500 hours of unlabeled voice data. There was a considerable improvement in the outcomes as the word error rate (WER) dropped by 33.9 percent.
Text Document Classification: A text document classifier can be constructed successfully using semi-supervised learning. The technique works well in this case since it is quite challenging for human annotators to read through numerous wordy texts to assign a simple label, such as a type or genre. The classifiers can be built on deep learning neural networks such as Long Short-Term Memory (LSTM) networks. LSTM networks can detect long-term dependencies in data and retrain on historical information over time. Training a neural network typically requires large amounts of data, labeled or unlabeled. Because semi-supervised learning frameworks can train a basic LSTM model on a handful of hand-labeled text samples containing the most relevant words and apply it to a larger set of unlabeled samples that works perfectly.
Web Content Classification: With billions of sites offering all kinds of content, information on web pages by assigning relevant labels would require a sizable team of human resources. To enhance user experience, web information is annotated and categorized using versions of semi-supervised learning. Many search engines, including Google, use SSL in their ranking component to better interpret human language and the suitability of potential search results to queries. Google Search uses SSL to locate the material most pertinent to a user query.

Types of Semi-supervised Learning

Inductive Semi-supervised learning: The semi-supervised learning is the same as inductive learning. We build and train a machine learning model based on an existing labeled training dataset. Then, the labels from the test dataset that we have never seen before are predicted by this trained model.
Transductive semi-supervised learning: Both training and test data sets are pre-observed through transformational learning techniques. First, learn from the previous training dataset to predict the label of the test dataset. We may use patterns and other information contained in this data during training, even if we do not know the label of the test dataset.

Working Principles of Semi-supervised Learning

Working with unlabeled datasets requires relationships between the objects in the dataset. To observe and understand this process, semi-supervised learning uses one of the following assumptions:

Continuity assumption: According to the continuity assumption, objects close to each other tend to have the same group or label. This assumption is also used in supervised learning, where decision boundaries separate datasets. In semi-supervised learning, decision boundaries are added, assuming the smoothness of the sparse boundaries.
Manifold assumptions: This manifold assumption helps use distances and densities where data reside on a manifold of lesser dimensions than the input space. The dimensional data can be difficult to model directly because a process with limited degrees of freedom creates it. This assumption becomes realistic when it gets high.
Cluster Assumptions: Based on this cluster assumption, the data are split into several distinct clusters. Additionally, the points within the same cluster share output specifications.

Techniques Used in Semi-supervised Learning


  • It trains the model with given labeled data
  • Use the trained model to predict labels for the unlabeled data
  • Retrain a model with pseudo-labeled and labeled data together

  • This process is iterated as the model improves, allowing greater accuracy to be achieved.

    Self-training: Self-training is a form of pseudo-labeling. The difference with self-training is that we only accept highly confident predictions and do this process multiple times. However, with pseudolabeling, no confidence bounds must be met for predictions to be used in the model.
    Label Propagation: This technique is a histogram-based transformation method to infer pseudo-labels for unlabeled data. Unlabeled data points iteratively use the labels of the majority of their neighbors based on the labeled data points.

    The label propagation makes some of a few assumptions:

  • All classes in the dataset are included in labeled data
  • Neighboring data points have similar labels
  • Data points in the same cluster will probably have the same label.

  • The label propagation creates a fully connected graph where nodes are all labeled and unlabeled data points. Edges between two nodes are weighted. The shorter the Euclidean distance between two nodes, the greater the weight. The larger edge weight allows the label to easily "move" through the templates.

    Why Semi-supervised Learning is important?

  • Semi-supervised techniques are applied to increase your training data.
  • Semi-supervised learning acts as a bridge to solve the main problems related to supervised learning and unsupervised learning.
  • With it, we initially train a model on a small sample of labeled data before iteratively applying it to a larger sample of unlabeled data.
  • It is effective for a wide range of problems, including clustering, association and regression, and classification, and reduces data preparation time. 

  • Advantages of Semi-supervised Learning

  • Semi-supervised learning is very effective when labels are limited and unlabeled data is abundant.
  • Simple and easy to understand.
  • The performance and generalization of your model can be improved. Without wasting time and money submitting tens of thousands of additional photos, your model will be exposed to the scenarios it may encounter during deployment.
  • In countless cases, the labeled data is not easily accessible. With only a tiny portion of labeled data, semi-supervised learning can complete typical tasks with state-of-the-art results.
  • Semi-supervised learning is used everywhere, from information-gathering tools and information-synthesis systems to image and speech recognition. 

  • Disadvantages of Semi-supervised Learning

  • The results of the iterations are not stable.
  • Network-level data does not apply to semi-supervised learning.
  • Since no method exists to confirm that the algorithm has generated 100 sort labels, it produces less reliable results than the normal supervised process.

  • Applications of Semi-supervised Learning

    Semi-supervised learning has various applications across domains where labeled data is scarce or expensive. Some of the applications of semi-supervised learning include,

    Recommendation Systems: Semi-supervised learning can be utilized in recommendation systems to suggest relevant items to users. The model can generate more accurate recommendations by leveraging user preferences from a small set of labeled data and incorporating information from unlabeled user behavior.
    Image and Object Recognition: This can be applied to image and object recognition tasks. By combining labeled and unlabeled image data, the model can learn to recognize and classify objects in images more accurately. This is particularly useful when obtaining labeled data is costly or time-consuming.
    Bioinformatics: Bioinformatics can assist in various tasks, such as protein structure prediction, gene expression analysis, or drug discovery. By incorporating unlabeled biological data, the model can extract useful features and patterns that aid in understanding complex biological processes.
    Anomaly Detection: Semi-supervised learning can be employed for anomaly detection tasks where the goal is to identify rare or abnormal instances in a dataset. By training on a small set of labeled normal data and a larger set of unlabeled data, the model can learn to differentiate between normal and anomalous patterns.
    Fraud Detection: Detecting fraudulent activities like credit card fraud or online scams benefits from semi-supervised learning. The model can learn to identify suspicious patterns and flag potential fraudulent instances using a combination of labeled fraud cases and unlabeled transaction data.
    Text Classification: Applied to tasks such as sentiment analysis, document classification, or spam detection by leveraging a small set of labeled examples and a larger set of unlabeled data, the model can learn to classify text data more effectively.
    Speech Recognition: It can be useful in speech recognition tasks where transcribed speech data and a smaller set of labeled data are limited. The model can improve its accuracy in recognizing and transcribing speech.
    Natural Language Processing: Semi-supervised learning is widely used in NLP applications. It can help improve tasks such as named entity recognition, part-of-speech tagging, machine translation, and text summarization. The model can learn to capture linguistic patterns and improve its performance by leveraging unlabeled text data and labeled examples.
    Social Network Analysis: This can be applied to analyze social networks and detect communities, influential nodes, or anomalies with both labeled and unlabeled network data models can uncover hidden patterns and structures within social networks.

    Recent advances in semi-supervised learning are semi-supervised learning models in automated machine learning with meta-learning and neural architecture, semi-supervised software package development, and semi-supervised neural networks.

    Potential Future Research Directions of Semi-supervised Learning

    Improved Label Propagation: Label propagation is a common technique in semi-supervised learning to propagate labels from labeled to unlabeled data. Future research may focus on developing more effective and efficient label propagation algorithms that consider the underlying data distribution, class imbalance, and noisy labels.
    Unsupervised Feature Learning: Unsupervised feature learning aims to learn representations or features from unlabeled data automatically.
    Active Learning with Limited Labeling Budget: Active learning involves selecting the most informative instances from the unlabeled data for labeling.
    Privacy-Preserving Semi-Supervised Learning: Privacy concerns are becoming increasingly important in machine learning.
    Robustness to Label Noise and Outliers: Semi-supervised learning can be sensitive to label noise and outliers in the labeled data as these can propagate to the unlabeled data during training.
    Semi-Supervised Reinforcement Learning: Reinforcement learning often requires many interactions with the environment to learn an optimal policy.