Disentangling factors of variation in deep representation using adversarial training
Authors: Michael F. Mathieu, Junbo Jake Zhao, Junbo Zhao, Aditya Ramesh, Pablo Sprechmann, Yann LeCun
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic and real datasets show that the proposed method is capable of generalizing to unseen classes and intra-class variabilities. We evaluate our model on both synthetic and real datasets: Sprites dataset [24], MNIST [15], NORB [16] and the Extended-Yale B dataset [8]. We report both training and testing errors in Table 1. |
| Researcher Affiliation | Academia | Michael Mathieu, Junbo Zhao, Pablo Sprechmann, Aditya Ramesh, Yann Le Cun 719 Broadway, 12th Floor, New York, NY 10003 {mathieu, junbo.zhao, pablo, ar2922, yann}@cs.nyu.edu |
| Pseudocode | Yes | The diagram of the network is shown in figure 1, and the described training procedure is summarized in on Algorithm 1, in the supplementary material. |
| Open Source Code | No | The paper does not provide an unambiguous statement or a direct link to open-source code for the methodology described. |
| Open Datasets | Yes | We evaluate our model on both synthetic and real datasets: Sprites dataset [24], MNIST [15], NORB [16] and the Extended-Yale B dataset [8]. |
| Dataset Splits | Yes | We used early-stopping on a validation set to prevent overfitting. This results in 25 different object identities in the training set and another 25 distinct objects identities in the testing set [for NORB]. The training and testing sets contains roughly 600 and 180 images per individual respectively [for Extended-Yale B]. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using "Torch7 [3]" but does not specify a version number for Torch7 or any other software dependencies needed to replicate the experiment. |
| Experiment Setup | No | The paper states that "The network architectures follow that of DCGAN [21] and are described in detail in the supplementary material," implying that such details are not in the main text. It mentions a "two-layer neural network with 256 hidden units" for a classification task, but not for the main model training setup. |