Neural Networks with Recurrent Generative Feedback
Authors: Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan Nguyen, Doris Tsao, Anima Anandkumar
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, CNN-F shows considerably improved adversarial robustness over conventional feedforward CNNs on standard benchmarks. 3 Experiment 3.1 Generative feedback promotes robustness |
| Researcher Affiliation | Collaboration | Yujia Huang1 James Gornet1 Sihui Dai1 Zhiding Yu2 Tan Nguyen3 Doris Y. Tsao1 Anima Anandkumar1,2 1California Institute of Technology 2NVIDIA 3Rice University |
| Pseudocode | Yes | Algorithm 1: Iterative inference and online update in CNN-F |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for its methodology. |
| Open Datasets | Yes | We train a CNN-F model with two convolution layers and one fully-connected layer on clean Fashion-MNIST images. ... We train the CNN-F on Fashion-MNIST and CIFAR-10 datasets respectively. |
| Dataset Splits | No | The paper mentions Fashion-MNIST and CIFAR-10 datasets and training/testing, but does not provide specific split information (e.g., percentages, sample counts) for training, validation, and test sets. It mentions using 'a validation image from Fashion-MNIST' but not a systematic split. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., GPU model, CPU model) used for running the experiments. |
| Software Dependencies | No | The paper mentions training on Fashion-MNIST and CIFAR-10, and using Adam optimizer [13] in the Appendix, but does not specify software dependencies with version numbers (e.g., PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For Fashion-MNIST, we train a network with 4 convolution layers and 3 fully-connected layers. We use 2 convolutional layers to encode the image into feature space and reconstruct to that feature space. For CIFAR-10, we use the Wide Res Net architecture [39] with depth 40 and width 2. We reconstruct to the feature space after 5 basic blocks in the first network block. For more detailed hyper-parameter settings, please refer to Appendix B.2. During training, we use PGD-7 to attack the first forward pass of CNN-F to obtain adversarial samples. |