Invariant Representations without Adversarial Training
Authors: Daniel Moyer, Shuyang Gao, Rob Brekelmans, Aram Galstyan, Greg Ver Steeg
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance on of our proposed invariance penalty on two datasets with a fair classification task. We also demonstrate Fader Network -like capabilities for manipulating specified factors in generative modeling on the MNIST dataset. |
| Researcher Affiliation | Academia | Information Sciences Institute University of Southern California {moyerd, gaos, brekelma}@usc.edu {gregv, galstyan}@isi.edu |
| Pseudocode | No | The paper describes methods and derivations but does not include any structured pseudocode or algorithm blocks (e.g., labeled 'Algorithm 1'). |
| Open Source Code | No | The paper does not provide any concrete access to source code (e.g., a specific repository link, an explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | Both datasets are from the UCI repository. The preprocessing for both datasets follow Zemel et al. 2013[22], which is also the source for the pre-processing in our baselines [15, 21]. The first dataset is the German dataset... The second dataset is the Adult dataset... We demonstrate a form of unsupervised image manipulation... on the MNIST dataset. |
| Dataset Splits | Yes | Optimization and parameter tuning is done via a held-out validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like 'Adam' optimizer and implies the use of deep learning frameworks, but it does not provide specific version numbers for any ancillary software dependencies (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | We use a latent space of 30 dimensions for each case. We train using Adam using the same hyperparameter settings as in Xie et al., and a batch size of 128. |