Methods › General › Model Compression › NNCF
Neural Network Compression Framework
NNCF
Introduced by Alexander Kozlov et al. in Neural Network Compression Framework for fast model inference
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Neural Network Compression Framework, or NNCF, is a Python-based framework for neural network compression with fine-tuning. It leverages recent advances of various network compression methods and implements some of them, namely quantization, sparsity, filter pruning and binarization. These methods allow producing more hardware-friendly models that can be efficiently run on general-purpose hardware computation units (CPU, GPU) or specialized deep learning accelerators.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Neural Collaborative Filtering vs. Matrix Factorization Revisited 19 May 2020 · 4 repositories · arXiv:2005.09683
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Neural Network Compression Framework for fast model inference 20 Feb 2020 · 2 repositories · arXiv:2002.08679Syntology ran 0 of 2 samples · 2 unverified
Tasks archive 2025-07-28
9 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Binarization | 1 |
| CPU | 1 |
| Collaborative Filtering | 1 |
| GPU | 1 |
| Link Prediction | 1 |
| Neural Network Compression | 1 |
| Quantization | 1 |
| Retrieval | 1 |
| model | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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