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 [1].
Training Binary Neural Networks through Learning with Noisy Supervision
Authors: Kai Han, Yunhe Wang, Yixing Xu, Chunjing Xu, Enhua Wu, Chang Xu
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on benchmark datasets indicate that the proposed binarization technique attains consistent improvements over baselines. |
| Researcher Affiliation | Collaboration | 1State Key Lab of Computer Science, Institute of Software, CAS & University of Chinese Academy of Sciences 2Noah s Ark Lab, Huawei Technologies 3University of Macau 4School of Computer Science, Faculty of Engineering, University of Sydney. |
| Pseudocode | Yes | Algorithm 1 Feed-Forward and Back-Propagation Process of Binary Neuron Mapping with Noisy Supervision. |
| Open Source Code | No | No explicit statement about releasing source code or a link to a code repository is found in the paper. |
| Open Datasets | Yes | CIFAR-10 dataset (Krizhevsky & Hinton, 2009) consists of 60,000 32 32 color images belonging to 10 categories, with 6,000 images per category. |
| Dataset Splits | Yes | For hyper-parameter tuning, 10,000 training images are randomly sampled for validation and the rest images are for training. |
| Hardware Specification | Yes | All the models are implemented using Py Torch (Paszke et al., 2019) and conducted on NVIDIA Tesla V100 GPUs. |
| Software Dependencies | No | All the models are implemented using Py Torch (Paszke et al., 2019). A specific version number for PyTorch or other libraries is not provided beyond the citation year. |
| Experiment Setup | Yes | For CIFAR-10, Res Net-20 is used as baseline model. The binary baseline models are trained for 400 epochs with a batch size of 128 and an initial learning rate 0.1. We use the SGD optimizer with the momentum of 0.9 and set the weight decay to 0. Our method is ο¬ne-tuned based on the pretrained baseline for 120 epochs using SGD optimizer. The learning rate starts from 0.01 and decayed by 0.1 every 30 epochs. |