We lifted 1 functions out of this paper's own repositories and ran 0 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 |
|---|---|---|
| eriklindernoren/PyTorch-GAN | reimplementation | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| compute_gradient_penalty | Not yet run | eriklindernoren/PyTorch-GAN/implementations/dragan/dragan.py code served (permissive licence) · get_code("c0ef577ceff3d8e3") |
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We propose studying GAN training dynamics as regret minimization, which is in contrast to the popular view that there is consistent minimization of a divergence between real and generated distributions. We analyze the convergence of GAN training from this new point of view to understand why mode collapse happens. We hypothesize the existence of undesirable local equilibria in this non-convex game to be responsible for mode collapse. We observe that these local equilibria often exhibit sharp gradients of the discriminator function around some real data points. We demonstrate that these degenerate local equilibria can be avoided with a gradient penalty scheme called DRAGAN. We show that DRAGAN enables faster training, achieves improved stability with fewer mode collapses, and leads to generator networks with better modeling performance across a variety of architectures and objective functions.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1705.07215")
get_code_for_paper("1705.07215")
have("1705.07215")
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