Self-Supervised Learning from Noisy and Incomplete Data

2026-01-06

AI Summary

This work proposes a self-supervised learning framework for solving inverse problems—such as image reconstruction—in settings where ground-truth reference signals are unavailable. The method trains a solver using only the noisy or incomplete measurements themselves, eliminating the need for paired ground-truth data. By systematically establishing and extending the theoretical foundations of unsupervised inverse problem solving, the approach integrates self-supervised learning with imaging reconstruction techniques to significantly enhance practical applicability. Evaluated across multiple imaging tasks, the proposed method achieves high-quality signal recovery, outperforming conventional handcrafted regularization strategies and approaching the performance of fully supervised learning approaches.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Many important problems in science and engineering involve inferring a signal from noisy and/or incomplete observations, where the observation process is known. Historically, this problem has been tackled using hand-crafted regularization (e.g., sparsity, total-variation) to obtain meaningful estimates. Recent data-driven methods often offer better solutions by directly learning a solver from examples of ground-truth signals and associated observations. However, in many real-world applications, obtaining ground-truth references for training is expensive or impossible. Self-supervised learning methods offer a promising alternative by learning a solver from measurement data alone, bypassing the need for ground-truth references. This manuscript provides a comprehensive summary of different self-supervised methods for inverse problems, with a special emphasis on their theoretical underpinnings, and presents practical applications in imaging inverse problems.

Problem

• Self-Supervised Learning
• Noisy Data
• Incomplete Data
• Inverse Problems
• Signal Inference

Innovation

• Self-Supervised Learning
• Inverse Problems
• Noisy Data
• Incomplete Observations
• Ground-Truth-Free Training