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..
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 | Venue PDF | 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 EMAIL |
| 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. |