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..
Invertibility of Convolutional Generative Networks from Partial Measurements
Authors: Fangchang Ma, Ulas Ayaz, Sertac Karaman
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further demonstrate, empirically, that the same conclusion extends to networks with multiple layers, other activation functions (leaky Re LU, sigmoid and tanh), and weights trained on real datasets. |
| Researcher Affiliation | Collaboration | Fangchang Ma* MIT EMAIL Ulas Ayaz MIT EMAIL EMAIL Sertac Karaman MIT EMAIL Both authors contributed equally to this work. Ulas Ayaz is presently affiliated with Lyft, Inc. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The sample code is available at https://github.com/fangchangma/invert-generative-networks. |
| Open Datasets | Yes | We rescale the raw grayscale images from the MNIST dataset [11] to size of 32 ˆ 32. A similar study is conducted on a generative network trained on the Celeb Faces [12] dataset. |
| Dataset Splits | No | The paper does not explicitly specify training, validation, or test dataset splits or percentages. It only mentions the datasets used and the training framework. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU models, CPU types). |
| Software Dependencies | No | The paper mentions using "conditional deep convolutional generative adversarial networks (DCGAN) framework" and "Adam with learn rate 0.1" but does not specify version numbers for any software or libraries. |
| Experiment Setup | Yes | The first layer has 16 channels and the second layer has 1 single channel. Both layers have a kernel size of 5 and a stride of 3. We used Adam with learn rate 0.1 to optimize the latent code z . The optimization process usually converges within 500 iterations. The input noise to the generator is set to have a relatively small dimension 10 to ensure a sufficiently expanding network. |