Incremental Network Quantization: Towards Lossless CNNs with Low-precision Weights
Authors: Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, Yurong Chen
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the Image Net classification task using almost all known deep CNN architectures including Alex Net, VGG-16, Google Net and Res Nets well testify the efficacy of the proposed method. |
| Researcher Affiliation | Industry | Aojun Zhou , Anbang Yao, Yiwen Guo, Lin Xu, and Yurong Chen Intel Labs China {aojun.zhou, anbang.yao, yiwen.guo, lin.x.xu, yurong.chen}@intel.com |
| Pseudocode | Yes | Algorithm 1 Incremental network quantization for lossless CNNs with low-precision weights. |
| Open Source Code | No | The code will be made publicly available. |
| Open Datasets | Yes | Image Net dataset has about 1.2 million training images and 50 thousand validation images. Each image is annotated as one of 1000 object classes. |
| Dataset Splits | Yes | Image Net dataset has about 1.2 million training images and 50 thousand validation images. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like Caffe and Torch ('Since our method is implemented with Caffe, we make use of an open source tool4 to convert the pre-trained Res Net-18 model from Torch to Caffe.'), but does not specify version numbers for these or other dependencies. |
| Experiment Setup | Yes | Alex Net: Alex Net has 5 convolutional layers and 3 fully-connected layers. We set the accumulated portions of quantized weights at iterative steps as {0.3, 0.6, 0.8, 1}, the batch size as 256, the weight decay as 0.0005, and the momentum as 0.9. |