Optimizing the Latent Space of Generative Networks
Authors: Piotr Bojanowski, Armand Joulin, David Lopez-Pas, Arthur Szlam
ICML 2018 | Conference PDF | Archive PDF | Plain Text | 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 <bojanowski@fb.com>. |
| 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. |