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Comprehensive Survey of Model Compression and Speed up for Vision Transformers

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arxiv 2404.10407 v1 pith:2M2QBC4G submitted 2024-04-16 cs.CV

Comprehensive Survey of Model Compression and Speed up for Vision Transformers

Feiyang Chen , Ziqian Luo , Lisang Zhou , Xueting Pan , Ying Jiang This is my paper
classification cs.CV
keywords modelvisioncomprehensivecompressioncomputationaltechniquestransformersaccuracy
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Vision Transformers (ViT) have marked a paradigm shift in computer vision, outperforming state-of-the-art models across diverse tasks. However, their practical deployment is hampered by high computational and memory demands. This study addresses the challenge by evaluating four primary model compression techniques: quantization, low-rank approximation, knowledge distillation, and pruning. We methodically analyze and compare the efficacy of these techniques and their combinations in optimizing ViTs for resource-constrained environments. Our comprehensive experimental evaluation demonstrates that these methods facilitate a balanced compromise between model accuracy and computational efficiency, paving the way for wider application in edge computing devices.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. MixFrag: Fragility-Guided Mixed-Precision Post-Training Quantization for Vision Transformers

    cs.CV 2026-07 conditional novelty 4.5

    KL-isolation fragility plus MCKP bit allocation yields mixed-precision ViT PTQ that lags recent ImageNet PTQ but reports large COCO AP gains at MP3/MP3.

  2. Recti-Q: Feature-Space Rectification for Out-of-Distribution-Robust Quantized Perception in Edge Robotics

    cs.CV 2026-07 conditional novelty 4.0

    Recti-Q measures a 'Quantization-Induced Robustness Gap' in 4-bit PTQ vision models and shows a small head-level LoRA adapter trained on source data recovers part of the lost out-of-distribution accuracy.

  3. Are the High-weight Neurons the Important Ones in Image Classification Neural Networks?

    cs.AI 2026-07 reject novelty 3.0

    Weight magnitude is a weak and nonlinear proxy for per-weight importance in CNNs, but the paper's quantitative claims are undermined by a mislabeled metric and an unconventional definition of 'neuron'.