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A General Error-Theoretical Analysis Framework for Constructing Compression Strategies
The exponential growth in parameter size and computational complexity of deep models poses significant challenges for efficient deployment.
Compression for Better: A General and Stable Lossless Compression Framework
A key challenge is effectively leveraging compression errors and defining the boundaries for lossless compression to minimize model loss.
Lossless Model Compression via Joint Low-Rank Factorization Optimization
Low-rank factorization is a popular model compression technique that minimizes the error $\delta$ between approximated and original weight matrices.
FP=xINT:A Low-Bit Series Expansion Algorithm for Post-Training Quantization
We introduce a deep model series expansion framework to address this issue, enabling rapid and accurate approximation of unquantized models without calibration sets or fine-tuning.
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