Research map / Architectures and efficiency / Pruning and efficient tuning
CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply joint in-parallel pruning-quantization to AlexNet during fine-tuning | AlexNet · ImageNet | top-1 accuracy: 57.2% -> 57.9% | Helped |
| Apply joint in-parallel pruning-quantization to AlexNet | AlexNet · ImageNet | network size: 243.9 MB -> 4.8 MB | Helped |
| Apply joint in-parallel pruning-quantization to GoogLeNet during fine-tuning | GoogLeNet · ImageNet | top-1 accuracy: 68.9% -> 68.9% | No clear effect |
| Apply joint in-parallel pruning-quantization to GoogLeNet | GoogLeNet · ImageNet | network size: 28.0 MB -> 2.8 MB | Helped |
| Apply joint in-parallel pruning-quantization to ResNet-50 during fine-tuning | ResNet-50 · ImageNet | top-1 accuracy: 73.1% -> 73.7% | Helped |
| Apply joint in-parallel pruning-quantization to ResNet-50 | ResNet-50 · ImageNet | network size: 102.5 MB -> 6.7 MB | Helped |
Closest papers by meaning
- Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-Based Approach (CVPR 2020 · similarity 0.91)
- OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization (AAAI 2021 · similarity 0.90)
- Harmonious Coexistence of Structured Weight Pruning and Ternarization for Deep Neural Networks (AAAI 2020 · similarity 0.90)
- Focused Quantization for Sparse CNNs (NeurIPS 2019 · similarity 0.89)
- Automatic Joint Structured Pruning and Quantization for Efficient Neural Network Training and Compression (CVPR 2025 · similarity 0.89)
- Quantization without Tears (CVPR 2025 · similarity 0.88)
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