Jacobian Adversarially Regularized Networks for Robustness
Authors: Alvin Chan, Yi Tay, Yew Soon Ong, Jie Fu
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Image classifiers trained with JARN show improved robust accuracy compared to standard models on the MNIST, SVHN and CIFAR-10 datasets, uncovering a new angle to boost robustness without using adversarial training examples. |
| Researcher Affiliation | Academia | 1Nanyang Technological University, 2Mila, Polytechnique Montreal |
| Pseudocode | Yes | Algorithm 1 details the corresponding pseudo-codes. |
| Open Source Code | Yes | Source code available at https://github.com/alvinchangw/JARN |
| Open Datasets | Yes | We conduct experiments on three image datasets, MNIST, SVHN and CIFAR-10 to evaluate the adversarial robustness of models trained by JARN. MNIST consists of 60k training and 10k test binary-colored images. SVHN is a 10-class house number image classification dataset with 73257 training and 26032 test images, each of size 32 32 3. CIFAR-10 contains 32 32 3 colored images labeled as 10 classes, with 50k training and 10k test images. |
| Dataset Splits | No | The paper specifies training and test set sizes for MNIST, SVHN, and CIFAR-10, but does not explicitly state the use of a separate validation set or its split details. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processors, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not specify particular software versions (e.g., Python, PyTorch, TensorFlow, or CUDA versions) required to replicate the experiment. |
| Experiment Setup | Yes | For JARN, we use λadv = 1, a discriminator network of 2 CNN layers (64-128 output channels) and update it for every 10 fcls training iterations. For JARN, we use λadv = 5, a discriminator network of 5 CNN layers (16-32-64-128-256 output channels) and update it for every 20 fcls training iterations. For JARN, we use λadv = 1, a discriminator network of 5 CNN layers (32-64-128-256-512 output channels) and update it for every 20 fcls training iterations. |