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..
Distribution-Interpolation Trade off in Generative Models
Authors: Damian Leśniak, Igor Sieradzki, Igor Podolak
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments conducted using the DCGAN model on the Celeb A dataset are presented solely to illustrate the problem, not to study the DCGAN itself, theoretically or empirically. All experiments were conducted using a DCGAN model (Radford et al., 2015), in which the generator network consisted of a linear layer with 8192 neurons, followed by four convolution transposition layers, each using 5 5 filters and strides of 2, with number of filters in order of layers: 256, 128, 64, 3. |
| Researcher Affiliation | Academia | Damian Le sniak Jagiellonian University Igor Sieradzki Jagiellonian University Igor Podolak Jagiellonian University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology described. |
| Open Datasets | Yes | The experiments conducted using the DCGAN model on the Celeb A dataset (Liu et al., 2015) |
| Dataset Splits | No | The paper mentions using the Celeb A dataset and specific training parameters like batch size, but does not specify the training, validation, or test dataset splits. |
| Hardware Specification | No | The paper describes the model architecture and training parameters, but it does not specify any hardware details (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using the 'Adam optimiser' and 'No batch normalisation' but does not specify any version numbers for software dependencies such as libraries or frameworks. |
| Experiment Setup | Yes | Adam optimiser with learning rate of 2e 4 and momentum set to 0.5 was used. Batch size 64 was used throughout all experiments. If not explicitly stated otherwise, latent space dimension was set to 100. For the Celeb A dataset we resized the input images to 64 64. |