Machine Learning Algorithms Applied to Corrupted Data Recovery: A Comprehensive Survey

Key Points

• The aim is to synthesize various machine learning methods for recovering corrupted data.
• Narrative survey of machine learning techniques.
• Discussion of supervised, unsupervised, generative, and reinforcement learning strategies.
• Conceptual synthesis of common approaches used for data recovery.
• Identified a range of machine learning approaches applicable to corrupted data recovery.
• Demonstrated potential effectiveness of generative and transformer-based models.
• Highlighted the evolving landscape of data recovery techniques through machine learning.