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Model Compression

arXiv 2105.10059 ↗First posted 20 May 2021cs.LGcited by 0

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Abstract (arXiv)

With time, machine learning models have increased in their scope, functionality and size. Consequently, the increased functionality and size of such models requires high-end hardware to both train and provide inference after the fact. This paper aims to explore the possibilities within the domain of model compression, discuss the efficiency of combining various levels of pruning and quantization, while proposing a quality measurement metric to objectively decide which combination is best in terms of minimizing the accuracy delta and maximizing the size reduction factor.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 7 references that are in the map. First posted on arXiv 20 May 2021.

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7 of 7 references.

References · 7

  1. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
  2. 2017To prune, or not to prune: exploring the efficacy of pruning for model compressioncited by 603
  3. 2018Model compression via distillation and quantizationcited by 289
  4. 2021Pruning and Quantization for Deep Neural Network Acceleration: A Surveycited by 263
  5. 2019And the Bit Goes Down: Revisiting the Quantization of Neural Networkscited by 78
  6. 2016Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networkscited by 18
  7. 2020Neural Network Compression Framework for fast model inferencecited by 18

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  1. 2024Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models
  2. 2026Prune-then-Quantize or Quantize-then-Prune? Understanding the Impact of Compression Order in Joint Model Compression
  3. 2023Revisiting Data Augmentation in Model Compression: An Empirical and Comprehensive Study
  4. 2022Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks
  5. 2026Hybrid Compression: Integrating Pruning and Quantization for Optimized Neural Networks
  6. 2026Big2Small: A Unifying Neural Network Framework for Model Compression
  7. 2023Towards Optimal Compression: Joint Pruning and Quantization
  8. 2025When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models