Tianci Liu, Jeffrey Regier
We lifted 3 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| lliutianc/gan-flow | canonical | 2 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| count_parameters | Ran | lliutianc/gan-flow/util.py code served (permissive licence) · get_code("287c57b1c208a06a") |
| get_logger | Ran | lliutianc/gan-flow/util.py code served (permissive licence) · get_code("0ef812b5a4f36435") |
| w_distance | Not yet run | lliutianc/gan-flow/util.py code served (permissive licence) · get_code("78ec0f99765935f3") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Generative adversarial networks (GANs) and normalizing flows are both approaches to density estimation that use deep neural networks to transform samples from an uninformative prior distribution to an approximation of the data distribution. There is great interest in both for general-purpose statistical modeling, but the two approaches have seldom been compared to each other for modeling nonimage data. The difficulty of computing likelihoods with GANs, which are implicit models, makes conducting such a comparison challenging. We work around this difficulty by considering several low-dimensional synthetic datasets. An extensive grid search over GAN architectures, hyperparameters, and training procedures suggests that no GAN is capable of modeling our simple low-dimensional data well, a task we view as a prerequisite for an approach to be considered suitable for general-purpose statistical modeling. Several normalizing flows, on the other hand, excelled at these tasks, even substantially outperforming WGAN in terms of Wasserstein distance-the metric that WGAN alone targets. Scientists and other practitioners should be wary of relying on WGAN for applications that require accurate density estimation.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2006.10175")
get_code_for_paper("2006.10175")
have("2006.10175")
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