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..
Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective
Authors: Tianlong Chen, Yu Cheng, Zhe Gan, Jingjing Liu, Zhangyang Wang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments endorse the effectiveness of our proposed framework, across various GAN architectures (SNGAN, Big GAN, and Style GANV2) and diverse datasets (CIFAR-10, CIFAR-100, Tiny-Image Net, Image Net, and multiple few-shot generation datasets). |
| Researcher Affiliation | Collaboration | Tianlong Chen1, Yu Cheng2, Zhe Gan2, Jingjing Liu3, Zhangyang Wang1 1University of Texas at Austin, 2Microsoft Corporation, 3Tsinghua University |
| Pseudocode | Yes | Algorithm 1 Data-Efficient Iterative Magnitude Pruning Procedures ... Algorithm 2 Training (Sparse) GAN with Dataand Feature-level Augmentations |
| Open Source Code | Yes | Codes are available at: https://github. com/VITA-Group/Ultra-Data-Efficient-GAN-Training. |
| Open Datasets | Yes | In this section, we conduct comprehensive experiments on Tiny-Image Net [88], Image Net [89], CIFAR-10 [90], and CIFAR-100 based on the unconditional SNGAN [23] and Style GAN-V2 [6], as well as the class-conditional Big GAN [2]. ... We compare these transfer learning approaches5 with our data-efficient training scheme. ... Our comparison experiments are conducted using Style GAN-V2 on the Animal Face [96] dataset (160 cats and 389 dogs), and the 100-shot Obama, Grumpy Cat, and Panda datasets provided by [1]. |
| Dataset Splits | Yes | FID and IS are measured using 10K samples; the official validation set is utilized as the reference distribution. ... IS and FID are measured using 10K samples; the validation set is utilized as the reference. |
| Hardware Specification | Yes | All GANs are trained with 8 pieces of NVIDIA V100 32GB. |
| Software Dependencies | No | The paper mentions using "Studio GAN codebase", "Diff Aug [1]", "ADA [15]", and "Py Torch implementation2" but does not specify version numbers for any of these software components. |
| Experiment Setup | Yes | Big GAN takes learning rates of {4, 2, 2} 10 4 for G, of {1, 5, 2} 10 4 for D, batch sizes of {256, 256, 64}, 1 105 training iterations, and {1, 2, 5} D steps per G step on {Tiny-Image Net, Image Net, CIFAR} datasets. ... SNGAN uses learning rates of 2 10 4 for G and D, batch sizes of 64, 5 104 training iterations, and five D steps per G step on CIFAR. ... Adv Aug with PGD-1 and step size 0.01/0.001 is applied... ...applying Adv Aug to the last layer of D and the first layer of G, with PGD-1 and step size 0.01, seems to be a sweet-point configuration for data-efficient GAN training, which is hence adopted as our default setting. |