arXiv:2606.17782v1 Announce Type: new Abstract: Primary motivation in blind inverse problems is to recover signals of interest from corrupted observations without knowing the obfuscating mechanism. Blind deconvolution is a prominent approach when the corruption is convolutional, but it is not applicable when general linear transformations obfuscate the domain structure. In this work, we propose an unsupervised framework for recovering latent domains and signals by discovering symmetries of the data distribution. Our framework models observations as linear measurements of signals sampled from a
The paper addresses a fundamental challenge in AI applications, specifically in autonomously interpreting corrupted data without prior knowledge of the corruption mechanism, which is critical for increasingly autonomous systems.
This research could significantly improve the robustness and generalizability of AI models in real-world scenarios, accelerating the development and deployment of advanced AI applications across various sectors.
The ability to blindly recover latent domains and signals through unsupervised symmetry discovery offers a new paradigm for handling data uncertainty and complexity in AI systems.
- · AI/ML researchers
- · Robotics
- · Autonomous systems developers
- · Data science
- · Traditional inversion methods
- · Systems requiring extensive data preprocessing
Improved performance and reliability of AI models in environments with noisy or obscured data.
Faster development and adoption of AI systems capable of operating in complex, unpredictable conditions.
Reduced human oversight requirements for advanced AI, potentially accelerating their integration into sensitive applications.
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Read at arXiv cs.LG