We lifted 1 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| stanifrolov/attrlostgan | extension | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| get_score | Ran | stanifrolov/attrlostgan/eval/attr_f1.py pointer only (licence: NONE) · get_code("f19ef3041e7db2b2") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Despite remarkable recent progress on both unconditional and conditional image synthesis, it remains a long-standing problem to learn generative models that are capable of synthesizing realistic and sharp images from reconfigurable spatial layout (i.e., bounding boxes + class labels in an image lattice) and style (i.e., structural and appearance variations encoded by latent vectors), especially at high resolution. By reconfigurable, it means that a model can preserve the intrinsic one-to-many mapping from a given layout to multiple plausible images with different styles, and is adaptive with respect to perturbations of a layout and style latent code. In this paper, we present a layout- and style-based architecture for generative adversarial networks (termed LostGANs) that can be trained end-to-end to generate images from reconfigurable layout and style. Inspired by the vanilla StyleGAN, the proposed LostGAN consists of two new components: (i) learning fine-grained mask maps in a weakly-supervised manner to bridge the gap between layouts and images, and (ii) learning object instance-specific layout-aware feature normalization (ISLA-Norm) in the generator to realize multi-object style generation. In experiments, the proposed method is tested on the COCO-Stuff dataset and the Visual Genome dataset with state-of-the-art performance obtained. The code and pretrained models are available at \url{https://github.com/iVMCL/LostGANs}.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1908.07500")
get_code_for_paper("1908.07500")
have("1908.07500")
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