Network Pruning
Network Pruning is a popular approach to reduce a heavy network to obtain a light-weight form by removing redundancy in the heavy network. In this approach, a complex over-parameterized network is first trained, then pruned based on come criterions, and finally fine-tuned to achieve comparable performance with reduced parameters.
Source: Ensemble Knowledge Distillation for Learning Improved and Efficient Networks
Papers
Showing 1–10 of 534 papers
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | TAS-pruned ResNet-110 | Accuracy | 94.33 | — | Unverified |
| 2 | ShuffleNet – Quantised | Inference Time (ms) | 23.15 | — | Unverified |
| 3 | AlexNet – Quantised | Inference Time (ms) | 5.23 | — | Unverified |
| 4 | MobileNet – Quantised | Inference Time (ms) | 4.74 | — | Unverified |