Model compression paper is accepted by ICCV 2023!

👏 Paper title: Lossy and Lossless (L2) Post-training Model Size Compression. We propose a unified post-training model size compression method that combines lossy and lossless compression techniques, with a parametric weight transformation and a differentiable counter to guide optimization. Our method achieves a stable 10× compression ratio without accuracy loss and a 20× ratio with minimal accuracy degradation, all while easily controlling the global compression ratio and adapting it for different layers. [related project]