Feature Space Particle Inference for Neural Network Ensembles
Authors: Shingo Yashima, Teppei Suzuki, Kohta Ishikawa, Ikuro Sato, Rei Kawakami
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluation on real-world datasets shows that our model significantly outperforms the gold-standard Deep Ensembles on various metrics, including accuracy, calibration, and robustness. and 4. Experiments In this section, we present the results on popular image classification tasks: CIFAR-10, CIFAR-100 (Krizhevsky & Hinton, 2009), and Image Net (Deng et al., 2009). |
| Researcher Affiliation | Collaboration | 1Denso IT Laboratory Inc., Tokyo, Japan 2Tokyo Institute of Technology, Tokyo, Japan. |
| Pseudocode | Yes | Algorithm 1 feature-WGD (in parallel) |
| Open Source Code | Yes | Code is available at: https: //github.com/Denso ITLab/feature PI. |
| Open Datasets | Yes | In this section, we present the results on popular image classification tasks: CIFAR-10, CIFAR-100 (Krizhevsky & Hinton, 2009), and Image Net (Deng et al., 2009). |
| Dataset Splits | No | The paper mentions using 'standard scheduling, augmentation, and regularization schemes in the literature' and references datasets like CIFAR and ImageNet, which typically have predefined splits. However, it does not explicitly state the specific percentages or counts for training, validation, and test splits used in their experiments. |
| Hardware Specification | Yes | In our experiment on CIFAR-100 for Wide Res Net-16-4 with an ensemble size of 10, feature-WGD takes approximately 17s for one epoch, while Deep Ensembles take 16s on four A100 GPUs. |
| Software Dependencies | No | The paper mentions using 'stochastic gradient descent with Nesterov momentum for optimization' and 'Adam' but does not specify software names with version numbers for libraries, frameworks, or programming languages (e.g., PyTorch version, Python version, CUDA version). |
| Experiment Setup | Yes | Table 7. Hyperparameter values for training on CIFAR-10, CIFAR-100, and Image Net. DATASET CIFAR-10 CIFAR-100 IMAGENET EPOCH 300 300 90 BATCH SIZE 128 128 256 BASE LEARNING RATE 0.1 0.1 0.1 LR DECAY RATIO 0.1 0.1 0.1 LR DECAY EPOCHS [150, 225] [150, 225, 250] [30, 60] MOMEMTUM 0.9 0.9 0.9 WEIGHT DECAY 5 10 4 5 10 4 1 10 4 |