Don't let your Discriminator be fooled
Authors: Brady Zhou, Philipp Krähenbühl
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that adversarial robustness both improves the visual quality of the results, as well as stabilizes the training procedure across a wide range of architectures, hyper-parameters and training objectives. We perform all our experiments on the MNIST (Le Cun et al., 1998), CIFAR10 (Krizhevsky, 2009) and Celeb A (Liu et al., 2015) datasets. Table 1 shows the quantitative results. |
| Researcher Affiliation | Academia | Brady Zhou Department of Computer Science University of Texas brady.zhou@utexas.edu Philipp Krähenbühl Department of Computer Science University of Texas philkr@cs.utexas.edu |
| Pseudocode | No | The paper includes mathematical formulations and theorems but no pseudocode or algorithm blocks. |
| Open Source Code | No | We will publish the code and data used to perform our experiments upon acceptance. |
| Open Datasets | Yes | We perform all our experiments on the MNIST (Le Cun et al., 1998), CIFAR10 (Krizhevsky, 2009) and Celeb A (Liu et al., 2015) datasets. |
| Dataset Splits | No | The paper mentions using MNIST, CIFAR10, and Celeb A datasets for experiments, but it does not specify the training, validation, or test split percentages or methodology. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions using ADAM for optimization, but it does not specify any software dependencies with version numbers (e.g., Python, PyTorch/TensorFlow versions). |
| Experiment Setup | Yes | We train using a weight decay term λ = 10 4, with batch size n = 64 and optimize using ADAM (Kingma & Ba, 2014) with h = 2 10 4 and β0 = 0, β1 = 0.9 for 50 epochs on CIFAR10 and 25 epochs on Celeb A. We use a latent vector of dimension z = 128 and use a unit Gaussian for our sampling distribution. All convolutional blocks are replaced with residual blocks (He et al., 2016), the generator employs batch normalization (Ioffe & Szegedy, 2015) and Re LU nonlinearities, while the discriminator uses instance normalization (Ulyanov et al., 2016) and Leaky Re LU. |