What are the process to optimize neural network model?
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Neural network models can be optimized through various processes. One approach is hybrid optimization, which involves pruning and quantization. Pruning eliminates unnecessary weights and connections, reducing the model size, while quantization converts the remaining weights into 8-bit integer values. This method has been successfully applied to heavy weight models like ResNet56, ResNet110, and GoogleNet, resulting in a significant reduction in parameters and flops without compromising model performance . Another method involves adding virtual layers between target layers and their preceding layers, reducing the number of connections and optimizing the model . Additionally, a novel approach called Neural Optimization Machine (NOM) uses a specially designed neural network architecture and training procedure to solve optimization problems. The NOM approximates objective functions with neural network models and conducts optimization using backpropagation . These methods effectively compress the neural network models, reduce parameter quantity, and save storage space .
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| Papers (4) | Insight |
|---|---|
| The process to optimize the neural network model includes determining target layers, adding virtual layers between the target layer and the preceding layer, and training the model to obtain an optimized neural network model. | |
24 Jun 2022 | The process to optimize a neural network model in this paper involves two phases: pruning and quantization. Pruning eliminates unnecessary weights and connections to reduce model size, while quantization converts the remaining weights into 8-bit integer values. |
| The paper describes a method to optimize a neural network model by determining target optimization layers, adding virtual layers between them and the upper layers, and training the model with the added virtual layers. | |
| The process to optimize a neural network model involves determining target optimization layers, adding virtual layers between these layers and the upper layers, and training the model with the added virtual layers to obtain an optimized model. |
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