Controllable Invariance through Adversarial Feature Learning
Authors: Qizhe Xie, Zihang Dai, Yulun Du, Eduard Hovy, Graham Neubig
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On three benchmark tasks, namely fair and bias-free classification, language-independent generation, and lighting-independent image classification, we show that the proposed framework induces an invariant representation, and leads to better generalization evidenced by the improved performance. |
| Researcher Affiliation | Academia | Qizhe Xie, Zihang Dai, Yulun Du, Eduard Hovy, Graham Neubig Language Technologies Institute Carnegie Mellon University {qizhex, dzihang, yulund, hovy, gneubig}@cs.cmu.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our MT code is available at https://github.com/qizhex/Controllable-Invariance |
| Open Datasets | Yes | Fair Classification For fair classification, we use three datasets... The German dataset [Frank et al., 2010]... The Adult income dataset [Frank et al., 2010]... The task of the health dataset1 is to predict whether a person will spend any days in the hospital in the following year. The sensitive variable is also the age and the dataset contains 147, 473 entries. 1www.heritagehealthprize.com, Multi-lingual Machine Translation For the multi-lingual machine translation task we use French to English (fr-en) and German to English (de-en) pairs from IWSLT 2015 dataset [Cettolo et al., 2012], Image Classification We use the Extended Yale B dataset [Georghiades et al., 2001] for our image classification task. |
| Dataset Splits | Yes | We follow the same 5-fold train/validation/test splits and feature preprocessing used in [Zemel et al., 2013, Louizos et al., 2016]. and We follow Li et al. [2014], Louizos et al. [2016] s train/test split and no validation is used |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Open NMT' and 'Moses multi-bleu.perl script' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | In all of our experiments, we use Adam [Kingma and Ba, 2014] with a learning rate of 0.001., We use a batch size of 16 and the number of hidden units is set to 64. γ is set to 1 in our experiments., Every model is run for 20 epochs. γ is set to 8 and the batch size is set to 64., γ is set to 2. The batch size is set to 16 and the hidden size is set to 100. |