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..
Optimizing the Latent Space of Generative Networks
Authors: Piotr Bojanowski, Armand Joulin, David Lopez-Pas, Arthur Szlam
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Throughout a variety of experiments, we show that GLO enjoys many of the desirable properties of GANs: synthesizing visually-appealing samples, interpolating meaningfully between samples, and performing linear arithmetic with noise vectors; all of this without the adversarial optimization scheme. |
| Researcher Affiliation | Industry | 1Facebook AI Research. Correspondence to: Piotr Bojanowski <EMAIL>. |
| Pseudocode | No | The paper describes the optimization process verbally and with mathematical equations but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | No explicit statement or link providing access to the source code for the described methodology was found in the paper. |
| Open Datasets | Yes | We carry out our experiments on MNIST (http://yann.lecun.com/exdb/mnist/), SVHN (http://ufldl.stanford.edu/housenumbers/) as well as more challenging datasets such as Celeb A (http://mmlab.ie.cuhk.edu.hk/projects/ Celeb A.html) and LSUNbedroom (http://lsun.cs.princeton.edu/2017/). |
| Dataset Splits | No | The paper specifies training on the complement of a 1/32 test set, but does not explicitly mention a separate validation split or how hyperparameters were tuned. |
| Hardware Specification | No | The paper does not specify any hardware details such as CPU, GPU models, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions using DCGAN for generator architecture and Stochastic Gradient Descent (SGD) for optimization, but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | We use Stochastic Gradient Descent (SGD) to optimize both θ and z, setting the learning rate for θ at 1 and the learning rate of z at 10. After each update, the noise vectors z are projected to the unit ℓ2 Sphere. In the sequel, we initialize the random vectors of GLO using a Gaussian distribution (for the Celeb A dataset) or the top d principal components (for the LSUN dataset). We use the ℓ2 + Lap1 loss for all the experiments but MNIST where we use an MSE loss. We use 32 dimensions for MNIST, 64 dimensions for SVHN and 256 dimensions for Celeb A and LSUN. |