FreGAN: Exploiting Frequency Components for Training GANs under Limited Data
Authors: mengping yang, Zhe Wang, Ziqiu Chi, Yanbing Zhang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering, ECUST, China Key Laboratory of Smart Manufacturing in Energy Chemical Process, ECUST, China wangzhe@ecust.edu.cn mengpingyang@mail.ecust.edu.cn |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our codes are available at https://github.com/kobeshegu/Fre GAN_Neur IPS2022. |
| Open Datasets | Yes | We test the effectiveness of our method on low-shot datasets from various domains with different resolutions. On 256 256 resolution, we use Animal Face Dogs and Cat [33], as well as 100-shot-Panda, Obama, and Grumpy_cat [51]. On 512 512 resolution, we use Anime-Face, Art Paintings, Moongate, Flat-colored, and Fauvism-still-life [24]. On 1024 1024 resolution, we use Pokemon, Skulls, Shells, Met Face [16] and Breca HAD [1]. |
| Dataset Splits | No | The paper does not provide specific details about validation dataset splits (percentages, sample counts, or explicit standard split references). |
| Hardware Specification | No | The paper states that implementation details in Section 4 provide information on the total amount of compute and type of resources, but Section 4 and its subsections do not explicitly mention specific hardware details such as GPU/CPU models, processor types, or memory amounts used for experiments. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with their version numbers, such as programming languages, libraries, or solvers. |
| Experiment Setup | Yes | We choose the current state-of-the-art few-shot generative model Fast GAN [24] as the backbone and implement our proposed techniques upon it. all other settings remain the same as [24]. We decompose the intermediate 8 8, 16 16, 32 32 features of G and D into frequency components for our frequency skip connection, high-frequency discriminator, and high-frequency alignment. |