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..
BiMLP: Compact Binary Architectures for Vision Multi-Layer Perceptrons
Authors: Yixing Xu, Xinghao Chen, Yunhe Wang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on benchmark dataset Image Net-1k demonstrate the effectiveness of the proposed Bi MLP models, which achieve state-of-the-art accuracy compared to prior binary CNNs. |
| Researcher Affiliation | Industry | Yixing Xu, Xinghao Chen, Yunhe Wang Huawei Noah s Ark Lab EMAIL |
| Pseudocode | No | The paper describes methods and architectures but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The Mind Spore code is available at https://gitee.com/mindspore/ models/tree/master/research/cv/Bi MLP. |
| Open Datasets | Yes | The benchmark dataset Image Net-1k [36] contains over 1.2M training images and 50k validation images from 1,000 different categories. |
| Dataset Splits | Yes | The benchmark dataset Image Net-1k [36] contains over 1.2M training images and 50k validation images from 1,000 different categories. |
| Hardware Specification | Yes | We use NVIDIA V100 GPUs with a total batchsize of 1024 to train the model with Mindspore [17]. |
| Software Dependencies | No | The paper mentions using 'Mindspore [17]' for training but does not provide specific version numbers for MindSpore or any other software libraries. |
| Experiment Setup | Yes | In both steps, the student models are trained for 300 epochs using Adam W [28] optimizer with momentum of 0.9 and weight decay of 0.05. We start with the learning rate of 1 10 3 and a cosine learning rate decay scheduler is used during training. We use NVIDIA V100 GPUs with a total batchsize of 1024 to train the model with Mindspore [17]. The commonly used data-augmentation strategies such as Cut-Mix [51], Mixup [52] and Rand-Augment [5] are used. |