About: Incremental approach to binarizing highly sensitive recurrent neural networks. The model is sequentially trained from a real-valued network initially, and re-trained (fine-tuned) with weights quantized at 10% increments until fully binarized.
This project was supported by Intel Corporation.
Paper: Incremental Binarization On Recurrent Neural Networks For Single-Channel Source Sepration. Link: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8682595
More updates/clean ups to be done, pleqse contact authors for immediate questions.
- First-phase pretraining a real-valued network.
- Second-phase incremental binarization process.
- To be posted soon.
- To pretrain the real-valued network,
python training_stage_1.py 5 5 3 0.0001 0.4 0.9 50 1
- To perform incremental binarization,
python training_stage_2.py 5 5 9e-5 0.1 Phase_1_Ep500