
The author argues that well-trained models need protection from unauthorized access, leading to the development of the MAP framework based on the Inverse Transfer Parameter Hypothesis.


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map-mask-pruning-for-source-free-model-intellectual-property-protection


MAP: MAsk-Pruning for Source-Free Model Intellectual Property Protection


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Deep learning models can be protected from unauthorized usage through the innovative MAsk Pruning (MAP) framework, which minimizes performance degradation on unauthorized data while maintaining source domain performance.



Deep learning models can be safeguarded through the innovative MAsk Pruning (MAP) framework, ensuring protection of intellectual property in source-free scenarios.



Die MAP-Methode bietet eine effektive Lösung für den Schutz des geistigen Eigentums von Modellen in verschiedenen Szenarien.
