A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations
Modern deep neural networks, particularly recent large language models, come with massive model sizes that require significant computational and storage resources.
Tags：Paper and LLMsAdversarial Robustness Network PruningPricing Type
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GitHub Link
The GitHub link is https://github.com/hrcheng1066/awesome-pruning
Introduce
The GitHub repository “awesome-pruning” is a comprehensive collection of neural network pruning research and open-source code. The repository covers various aspects of pruning neural networks, including static and dynamic pruning, learning and pruning strategies, and applications in computer vision, natural language processing, and audio signal processing. The repository organizes pruning methods based on different criteria, such as timing of pruning and specific techniques, and provides an extensive list of relevant papers and associated resources. The work aims to offer a valuable resource for researchers and practitioners interested in the field of neural network pruning.
Modern deep neural networks, particularly recent large language models, come with massive model sizes that require significant computational and storage resources.
Content
Taxonomy: In our survey, we provide a comprehensive review of the state-of-the-art in deep neural network pruning, which we categorize along five orthogonal axes: Universal/Specific Speedup, When to Prune, Pruning Criteria, Learn to Prune, and Fusion of Pruning and Other Techniques.
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