Model compression and acceleration refers to techniques used to reduce the size and complexity of deep learning models while maintaining their performance. This is important for deploying models in resource-constrained environments like mobile devices or embedded systems, where computational power and memory are limited. The goal is to create more efficient models that can perform faster inference without a significant loss in accuracy.
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