neural network compression
Neural network compression is the process of reducing the size or complexity of a neural network without significant loss in its performance. It aims to make neural networks more efficient in terms of storage, computation, and energy requirements, while maintaining or even improving their accuracy. This can be achieved through various techniques such as pruning, quantization, low-rank approximation, knowledge distillation, and parameter sharing. Neural network compression enables deployment of models on resource-constrained devices or in situations where efficiency is crucial, making them more practical and accessible.
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