Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Incremental Network Quantization: Towards Lossless CNNs with Low-precision Weights
Authors: Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, Yurong Chen
ICLR 2017 | Venue PDF | 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 EMAIL |
| 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. |