Towards Inference Efficient Deep Ensemble Learning
Authors: Ziyue Li, Kan Ren, Yifan Yang, Xinyang Jiang, Yuqing Yang, Dongsheng Li
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments with different backbones on real-world datasets illustrate our method can bring up to 56% inference cost reduction while maintaining comparable performance to full ensemble, achieving significantly better ensemble utility than other baselines. Code and supplemental materials are available at https://seqml.github.io/irene. Experiment Experimental Setup Here we present the details of experimental setup, including datasets, backbones used, and baselines for comparison. |
| Researcher Affiliation | Industry | Microsoft Research litzy0619owned@gmail.com, kan.ren@microsoft.com |
| Pseudocode | Yes | The overall training algorithm has been illustrated in Appendix B. |
| Open Source Code | Yes | Code and supplemental materials are available at https://seqml.github.io/irene. |
| Open Datasets | Yes | We conduct experiments on two image classification datasets, CIFAR-10 and CIFAR-100, the primary focus of neural ensemble methods (Zhang, Liu, and Yan 2020; Rame and Cord 2021). CIFAR (Krizhevsky, Hinton et al. 2009) contains 50,000 training samples and 10,000 test samples, which are labeled as 10 and 100 classes in CIFAR-10 and CIFAR-100, respectively. |
| Dataset Splits | No | The paper mentions 50,000 training samples and 10,000 test samples for CIFAR datasets, but does not explicitly provide details about a validation split or how it was derived. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or cloud instances) are mentioned for running the experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python version, library versions like PyTorch or TensorFlow) are provided. |
| Experiment Setup | No | The paper mentions datasets, backbones (ResNet-32 and ResNet-18), and that ensemble methods use three base models (T=3), but it does not provide specific hyperparameters like learning rates, batch sizes, number of epochs, optimizer settings, or the values for the loss weights (ω1, ω2, ω3). |