Self-Supervised Learning for Small Data Environments

Abstract

Self-Supervised Learning (SSL) presents itself as a dominant learning paradigm that allows models to extract valuable information from unlabeled data collections. The general success of SSL in handling large-scale datasets does not address its potential application in limited data settings. This study analyzes SSL approaches designed for small data systems while focusing on their implementation methods in addition to their challenges and developments. This paper investigates three methods to improve learning efficiency through minimal supervision by focusing on data augmentation and contrastive learning and pre training strategies. Experimental studies show that SSL produces superior performance than standard supervised learning approaches when used in limited data circumstances.

Introduction

Traditional machine learning requires millions of labeled samples, which isn't always feasible due to:

• High annotation costs

• Data privacy concerns

• Limited availability of domain-specific data
  This is especially problematic in medicine, remote sensing, and niche fields.

? Solution: Self-Supervised Learning (SSL)

SSL enables models to learn meaningful representations from unlabeled data through pretext tasks. It can later fine-tune on small labeled datasets, dramatically reducing the need for labeled data.

???? SSL Techniques

1. Contrastive Learning

   • Learns to bring similar data points closer in representation space.

   • Examples:

     • SimCLR: Uses augmented views.

     • MoCo: Uses memory bank for stable training.

     • BYOL: Learns without negative samples.

   • Used in: Image and video recognition, speaker identification.

2. Predictive Coding

   • Learns to predict missing or masked parts of input.

   • Examples:

     • MLM (Masked Language Modeling): Predicts masked words (e.g., BERT).

     • MIM (Masked Image Modeling): Predicts masked pixels (e.g., MAE).

     • Wave2Vec: Predicts masked audio for speech recognition.

3. Clustering-Based SSL

   • Assigns pseudo-labels to unlabeled data via clustering.

   • Examples:

     • DeepCluster, SwAV, SEER.

   • Used in: Image classification, anomaly detection, recommendation systems.

?? Challenges with Small Datasets

• Privacy-sensitive domains (e.g., healthcare).

• Scarce labeled data in niche areas.

• High expert annotation costs.

???? How SSL Helps

• Uses large unlabeled datasets for pretraining.

• Builds generalized, transferable representations.

• Reduces overfitting and improves performance on limited labeled samples.

???? Popular SSL Methods in Small Data Contexts

• Contrastive Learning: Especially helpful in biomedical signals and diagnostics.

• Masked Image Modeling (MIM): Enables learning of spatial context.

• Self-Distillation (e.g., DINO): Trains a model to learn from its own predictions.

???? Advantages of SSL

1. Better generalization on limited data.

2. Lower dependency on annotations.

3. Increased robustness to noise and data variability.

???? Applications in Low-Data Environments

???? Medical Imaging

• Chest X-ray segmentation (DINO)

• Colon polyp diagnosis (Contrastive learning)

• Shoulder implant classification (SSP)

• GI lesion classification (96.4% accuracy)

• Liver fibrosis/NAS scoring (SSL from CT images)

• Crohn’s disease detection (contrastive SSL)

????? Structural Health Monitoring

• SSL applied for bridge anomaly detection with improved F1 scores.

????? Food Fraud Detection

• Proto-DS technique improved classification accuracy to 88.18% on limited hyperspectral data.

????? Remote Sensing

• Scene classification with fewer than 20 labeled samples per class (RS-FewShotSSL).

• Land use estimation using RGB patches showed better performance than supervised ImageNet models.

???? Other Use Cases

• PPG Signal artifact detection (health monitoring).

• Molecular property prediction using topological data.

• Human activity recognition via masked reconstruction.

• Few-shot learning improved with Manifold Mixup SSL.

Conclusion

Self-Supervision provides an effective way to address limitations associated with the availability of samples of small data in different areas. Self-Supervised Learning allows models to learn representations from the raw structures of the materials and render maximal utilizations of these representations for relearning by using limited labeled data. It is for this reason that as Self-Supervised Learning techniques are enhanced and the efficiency increases, they will become more useful in boosting machine learning in scenarios that lack ample data. There is also a look being made to optimize Self-Supervised Learning more in terms of computational complexity. Research is also being conducted about the more specific issue of selecting a better pretext task that captures data characteristics and is more transferable. Another branch of research is the incorporation of domain knowledge to the Self-Supervised Learning frameworks in a way to control the learning process and enhance representation. More advanced approaches could be applied when creating Automated Self-Supervised Learning pipelines to make selection of the best techniques and hyperparameters for particular tasks easier. Last but not least, the Self-Supervised Learning is being applied in the new domains where the data are scarce, for instance, the robotics applications & drug discovery.

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Copyright

Copyright © 2025 Thota Sharon, Terli Lavanya, Ganeshwar Sai, Vasudev Sharma, Vempadapu Sai Charan, M. Rupasri. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.