Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Few-shot Image Generation via Adaptation-Aware Kernel Modulation
Authors: Yunqing Zhao, Keshigeyan Chandrasegaran, Milad Abdollahzadeh, Ngai-Man (Man) Cheung
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results show that the proposed method consistently achieves SOTA performance across source/target domains of different proximity, including challenging setups when source and target domains are more apart. |
| Researcher Affiliation | Academia | Yunqing Zhao EMAIL Keshigeyan Chandrasegaran EMAIL Milad Abdollahzadeh EMAIL Ngai-Man Cheung: EMAIL Singapore University of Technology and Design (SUTD) |
| Pseudocode | Yes | Algorithm 1: Few-Shot Image Generation via Adaptation-Aware Kernel Modulation (Ad AM) |
| Open Source Code | Yes | Project Page: https://yunqing-me.github.io/Ad AM/ and We are releasing code and pre-trained GAN models. The URL details are included in Supplementary. |
| Open Datasets | Yes | We use Style GAN-V2 [3] as the GAN architecture and FFHQ as the source domain. Our experiments include setups with different source-target proximity: Babies/Sunglasses [14], Met Faces [36] and Cat/Dog/Wild (AFHQ) [5] |
| Dataset Splits | No | The paper mentions a '10-shot target adaptation setup' and 'batch size 4' but does not specify the train/validation/test dataset splits explicitly in the main text, only that training details are in Supplementary. |
| Hardware Specification | Yes | Adaptation is performed with 256 x 256 resolution and batch size 4 on a single Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions 'Style GAN-V2 [3] as the GAN architecture' but does not provide specific version numbers for software dependencies or libraries used. |
| Experiment Setup | Yes | Adaptation is performed with 256 x 256 resolution and batch size 4 on a single Tesla V100 GPU. We apply importance probing and modulation on base kernels of both generator and discriminator. We focus on 10-shot target adaptation setup in the main paper. |