Improving Heterogeneous Model Reuse by Density Estimation
Authors: Anke Tang, Yong Luo, Han Hu, Fengxiang He, Kehua Su, Bo Du, Yixin Chen, Dacheng Tao
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both synthetic and benchmark data demonstrate the superiority of the proposed method. |
| Researcher Affiliation | Collaboration | Anke Tang 1,2 , Yong Luo 1,2 , Han Hu 3 , Fengxiang He 4 , Kehua Su 1 , Bo Du 1,2 , Yixin Chen 5 , Dacheng Tao 6 1 School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, China 2 Hubei Luojia Laboratory, Wuhan, China 3 School of Information and Electronics, Beijing Institute of Technology, China 4 JD Explore Academy, JD.com, Inc., China 5 Department of CSE, Washington University in St. Louis, USA 6 The University of Sydney, Australia |
| Pseudocode | Yes | Algorithm 1 Heterogeneous Model Reuse aided by Density Estimation (without Calibration). |
| Open Source Code | Yes | The code is available at https://github.com/tanganke/HMR. |
| Open Datasets | Yes | We evaluate our method, HMR, and RKME on Fashion MNIST [Xiao et al., 2017], a popular benchmark dataset in the machine learning community, containing 70, 000 28 28 gray-scale fashion product images, each associated with a label from 10 classes. |
| Dataset Splits | Yes | The complete training set is split into a training set of 60, 000 examples and a test set of 10, 000 examples. To simulate the multiparty setting, we separate the training set into different parties with biased sample distribution. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. It only mentions 'a 3-layer convolutional network' for local classifiers. |
| Software Dependencies | No | The paper mentions "scikit-learn package" but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | Yes | We set the training batch size to be 128, and the learning rate of all local models to 1e-4 during the local training. The learning rate is 1e-5 during the calibration step. All local classifiers have the same 3-layer convolutional network and all local density estimators are the same 12-layer real non-volume preserving (real NVP) flow network [Dinh et al., 2016]. As For RKME, we set the reduced dimension size to 10, and the number of generated samples to 200. |