A Classification-Based Study of Covariate Shift in GAN Distributions
Authors: Shibani Santurkar, Ludwig Schmidt, Aleksander Madry
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now describe results from our experimental studies of covariate shift in GANs based on the procedures outlined in Section 3, using the setup from Section 4. |
| Researcher Affiliation | Academia | 1Massachusetts Institute of Technology. Correspondence to: Shibani Santurkar <shibani@mit.edu>, Ludwig Schmidt <ludwigs@mit.edu>, Aleksander Madry <madry@mit.edu>. |
| Pseudocode | No | The paper describes experimental procedures as numbered steps within paragraphs but does not provide formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | Yes | We chose five popular GANs and studied them on the Celeb A and LSUN datasets arguably the two most well known datasets in the context of GANs. Conveniently, these datasets also have rich annotations, making them particularly suited for our classification based evaluations. (Section 4.1) ... Celeb A (Liu et al., 2015) and LSUN (Yu et al., 2015) datasets... |
| Dataset Splits | No | The paper mentions 'train' and 'test' data, but does not explicitly specify validation dataset splits or methodology for validation in the main text. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as CPU or GPU models. |
| Software Dependencies | No | The paper mentions using standard implementations and specific models (e.g., '32-Layer Res Net', 'Linear Model') but does not specify software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | In the following sections we describe the setup and results for our classification-based GAN diversity studies. (Section 4) ... The paper details the steps for measuring 'Mode Collapse' (Section 3.1) and 'Boundary Distortion' (Section 3.2), including how synthetic datasets are generated and how classifiers are trained. It also states: 'The same architecture and hyperparameter settings were used for all datasets (true and GAN derived) in any given comparison of classification performance.' (Section 4.2) |