Uncertainty Quantification in CNN Through the Bootstrap of Convex Neural Networks
Authors: Hongfei Du, Emre Barut, Fang Jin12078-12085
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally demonstrate our approach has a much better performance compared to other baseline CNNs and state-of-the-art methods on various image datasets. |
| Researcher Affiliation | Collaboration | Hongfei Du 1, Emre Barut 2, Fang Jin 1 1 The George Washington University 2 Amazon.com, Inc. |
| Pseudocode | Yes | Algorithm 1 CCNN Bootstrap |
| Open Source Code | No | The paper does not contain any statements about making the source code available, nor does it provide any links to a code repository. |
| Open Datasets | Yes | We use five datasets: MNIST (Le Cun et al. 1998), noisy MNIST (Basu et al. 2017), fashion MNIST (Xiao, Rasul, and Vollgraf 2017), CIFAR10 (Krizhevsky 2009) and the cats and dogs dataset (Parkhi et al. 2012) |
| Dataset Splits | Yes | For the first three datasets... We use 60,000 images for training and 1,000 images for testing. For the Cats and Dogs dataset... We use 10,000 images for training and 1,000 images for testing. |
| Hardware Specification | Yes | The CCNN runs on the 16 cores CPU with 64GB RAM, and other classic neural networks run on GPU. |
| Software Dependencies | No | The paper mentions neural network architectures like 'Le-Net' and 'VGG16' but does not specify any software libraries (e.g., TensorFlow, PyTorch) or their version numbers used for implementation or experimentation. |
| Experiment Setup | Yes | We reduce the size of the training dataset (only 1000 images in the train set and 100 images in test set) and also reduce the training iterations to 5 at each bootstrap. We set the number of bootstraps B = 1000... For the first three datasets, the ensemble method and the bootstrap CNN use the classic CNN, Le-Net, with 3 convolution and 2 fully connected layers, where the numbers of convolution filters are (32,64,128) with a kernel size of (2,2). |