arXiv:2605.25299v1 Announce Type: cross Abstract: Recently, Deep Image Prior (DIP) has demonstrated strong capabilities for solving inverse imaging problems (IIPs) by optimizing a randomly initialized convolutional neural network in a training-data-free regime. However, DIP suffers from overfitting to noisy measurements due to network over-parameterization, making early stopping (ES) essential. The most successful ES method tracks fluctuations in the running variance of the network output to detect overfitting. However, in many applications, these fluctuations may appear prematurely, leading t
This research addresses a long-standing challenge in Deep Image Prior (DIP) related to overfitting and the limitations of existing early stopping mechanisms, which is crucial for robust real-world applications.
Improved early stopping for DIP can enhance the reliability and performance of AI in inverse imaging problems, making it more practical for critical applications where training data is scarce.
The proposed self-referenced early stopping method offers a more robust and less premature detection of overfitting in DIP, leading to better quality reconstructions in various imaging tasks.
- · AI researchers
- · Medical imaging sector
- · Computer vision applications
- · Data-scarce imaging domains
- · AI methods relying on large datasets
- · Current sub-optimal DIP early stopping techniques
More accurate and reliable image reconstruction in fields like medical imaging and scientific instrumentation.
Accelerated adoption of deep learning in inverse problems where traditional training data is costly or unavailable.
Potentially democratizes advanced imaging capabilities to domains with limited data resources, fostering broader AI integration.
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Read at arXiv cs.LG