Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Don't let your Discriminator be fooled
Authors: Brady Zhou, Philipp Krähenbühl
ICLR 2019 | Venue PDF | 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 EMAIL Philipp Krähenbühl Department of Computer Science University of Texas EMAIL |
| 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. |