mlsanityData to 14 Oct 2026

Evaluation Metrics for DNNs Compression

arXiv 2305.10616 ↗First posted 18 May 2023cs.LG cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

There is a lot of ongoing research effort into developing different techniques for neural networks compression. However, the community lacks standardised evaluation metrics, which are key to identifying the most suitable compression technique for different applications. This paper reviews existing neural network compression evaluation metrics and implements them into a standardisation framework called NetZIP. We introduce two novel metrics to cover existing gaps of evaluation in the literature: 1) Compression and Hardware Agnostic Theoretical Speed (CHATS) and 2) Overall Compression Success (OCS). We demonstrate the use of NetZIP using two case studies on two different hardware platforms (a PC and a Raspberry Pi 4) focusing on object classification and object detection.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 41 references that are in the map. First posted on arXiv 18 May 2023.

Builds on

20 of 41 references.

References · 20

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  4. 2018The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networkscited by 2,262
  5. 2016XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networkscited by 849
  6. 2019Learned Step Size Quantizationcited by 628
  7. 2020What is the State of Neural Network Pruning?cited by 583
  8. 2018AMC: AutoML for Model Compression and Acceleration on Mobile Devicescited by 378
  9. 2016Ternary Weight Networkscited by 204
  10. 2023DepGraph: Towards Any Structural Pruningcited by 203
  11. 2021Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Bettercited by 154
  12. 2021The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalizationcited by 131
8 more
  1. 2020Training Binary Neural Networks with Real-to-Binary Convolutionscited by 112
  2. 2020An Overview of Neural Network Compressioncited by 59
  3. 2022Differentiable Top-k Classification Learningcited by 21
  4. 2020Larq Compute Engine: Design, Benchmark, and Deploy State-of-the-Art Binarized Neural Networkscited by 20
  5. 2019Neural Network Distiller: A Python Package For DNN Compression Researchcited by 12
  6. 2021On Determinism of Game Engines used for Simulation-based Autonomous Vehicle Verificationcited by 6
  7. 2022One-shot Network Pruning at Initialization with Discriminative Image Patchescited by 2
  8. 2014EACOF: A Framework for Providing Energy Transparency to enable Energy-Aware Software Developmentcited by 1

Built on by

Not cited yet.