Training Neural Networks Using Features Replay
Authors: Zhouyuan Huo, Bin Gu, Heng Huang
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we apply our method to training deep convolutional neural networks, and the experimental results show that the proposed method achieves faster convergence, lower memory consumption, and better generalization error than compared methods. |
| Researcher Affiliation | Collaboration | Zhouyuan Huo1,2, Bin Gu2, Heng Huang1,2 1Electrical and Computer Engineering, University of Pittsburgh, 2 JDDGlobal.com zhouyuan.huo@pitt.edu, jsgubin@gmail.com heng.huang@pitt.edu |
| Pseudocode | Yes | Algorithm 1 Features Replay Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code related to the described methodology. |
| Open Datasets | Yes | We implement our method in Py Torch [28], and evaluate it with Res Net models [8] on two image classification benchmark datasets: CIFAR-10 and CIFAR-100 [18]. |
| Dataset Splits | No | The paper mentions using CIFAR-10 and CIFAR-100 datasets but does not explicitly provide the specific training, validation, and test dataset splits used for reproduction, nor does it cite a source for these specific splits. |
| Hardware Specification | Yes | All experiments are performed on a server with four Titan X GPUs. |
| Software Dependencies | No | The paper states 'We implement our method in Py Torch [28]', but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We use SGD with the momentum of 0.9, and the stepsize is initialized to 0.01. Each model is trained using batch size 128 for 300 epochs and the stepsize is divided by a factor of 10 at 150 and 225 epochs. The weight decay constant is set to 5e-4. |