Conditional Generation Using Polynomial Expansions
Authors: Grigorios Chrysos, Markos Georgopoulos, Yannis Panagakis
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Co PE is evaluated in five tasks (class-conditional generation, inverse problems, edges-to-image translation, image-to-image translation, attribute-guided generation) involving eight datasets. The thorough evaluation suggests that Co PE can be useful for tackling diverse conditional generation tasks. |
| Researcher Affiliation | Academia | Grigorios G Chrysos EPFL, Switzerland grigorios.chrysos@epfl.ch Markos Georgopoulos Imperial College London, UK m.georgopoulos@imperial.ac.uk Yannis Panagakis University of Athens, GR yannisp@di.uoa.gr |
| Pseudocode | No | The paper does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | The source code of Co PE is available at https://github.com/grigorisg9gr/ polynomial_nets_for_conditional_generation. |
| Open Datasets | Yes | To validate the proposed formulation, the following diverse conditional generation tasks are considered: class-conditional generation trained on CIFAR10, Cars196 and SVHN in sec. 4.1 and sec. H.2. ... inverse problems in imaging, e.g., super-resolution and block-inpainting, trained on Cars196 and Celeb A in sec. 4.3. ... Additionally, the source code of Co PE is available at https://github.com/grigorisg9gr/polynomial_nets_ for_conditional_generation. |
| Dataset Splits | Yes | We use standard data splits from well-established datasets (i.e. using the wrappers of Pytorch). The source code is publicly available. |
| Hardware Specification | Yes | A single GPU was used in each experiment. The GPU is part of our lab cluster. |
| Software Dependencies | No | The paper mentions using "wrappers of Pytorch" for standard data splits, but it does not specify version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | To avoid such instability, we take the following three steps: a) Co PE samples the noise vector from the uniform distribution, i.e., from the bounded interval of r 1, 1s, b) a hyperbolic tangent is used in the output of the generator as a normalization, i.e., it constrains the outputs in the bounded interval of r 1, 1s, c) batch normalization [Ioffe and Szegedy, 2015] is used to convert the representations to zero-mean. ... Each experiment is conducted five times and the mean and the standard deviation are reported. |