A Generalization of Continuous Relaxation in Structured Pruning

Structured pruning asserts that while large networks enable us to find solutions to complex computer vision problems, a smaller, computationally efficient sub-network can be extracted. We propose a generalization of continuous relaxation in structured pruning to effectively identify these optimal sub-networks, ensuring performance on resource-constrained hardware.

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