What is recorded here?
Pruned small-magnitude trained connections and retrained survivors, then combined pruning with quantized weight sharing/codebook retraining and Huffman coding to reduce stored model size.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Bundles the2015 pruning precursor and compression pipeline with distinct four/three-author bylines. Inherits earlier pruning/quantization/entropy-coding techniques; no invention of all pruning. Storage savings and retained accuracy are model/evaluation-specific, not guaranteed latency or energy reduction on dense hardware. First preprint title differs from laterDeep Compression title.
Read the original work
- Learning both Weights and Connections for Efficient Neural Networks
- A Deep Neural Network Compression Pipeline: Pruning, Quantization, Huffman Encoding
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Song Han; Jeff Pool; John Tran; William J. Dally; Huizi Mao
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
Pruning and Deep Compression: retraining, weight sharing and entropy coding
Pruned small-magnitude trained connections and retrained survivors, then combined pruning with quantized weight sharing/codebook retraining and Huffman coding to reduce stored model size.
Scientific scope
Bundles the2015 pruning precursor and compression pipeline with distinct four/three-author bylines. Inherits earlier pruning/quantization/entropy-coding techniques; no invention of all pruning. Storage savings and retained accuracy are model/evaluation-specific, not guaranteed latency or energy reduction on dense hardware. First preprint title differs from laterDeep Compression title.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Song Han; Jeff Pool; John Tran; William J. Dally; Huizi Mao
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.