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Two-phase collaborative model compression training for joint pruning and quantization
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DOI：10.1016/j.neunet.2025.108506.png)
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Abstract
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• We propose a unified collaborative model compression training to integrate pruning, quantization and performance objectives, balancing complexity reduction and precision loss in training. • A novel constraint function combining sparse regularization and quantization error enables automated pruning and efficient quantization, improving accuracy and hardware efficiency. • Our two-step training based on pre-trained networks jointly optimizes pruning and quantization, avoiding error accumulation and achieving better compression efficiency and model performance.
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