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..
Differentiable Augmentation for Data-Efficient GAN Training
Authors: Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, Song Han
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate consistent gains of our method over a variety of GAN architectures and loss functions for both unconditional and class-conditional generation. With Diff Augment, we achieve a state-of-the-art FID of 6.80 with an IS of 100.8 on Image Net 128 128 and 2-4 reductions of FID given 1,000 images on FFHQ and LSUN. Furthermore, with only 20% training data, we can match the top performance on CIFAR-10 and CIFAR-100. |
| Researcher Affiliation | Collaboration | Shengyu Zhao IIIS, Tsinghua University and MIT Zhijian Liu MIT Ji Lin MIT Jun-Yan Zhu Adobe and CMU Song Han MIT |
| Pseudocode | No | The paper includes a diagram (Figure 4) illustrating the data flow for Diff Augment but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/mit-han-lab/data-efficient-gans. |
| Open Datasets | Yes | We conduct extensive experiments on Image Net [6], CIFAR-10 [19], CIFAR-100, FFHQ [17], and LSUN-Cat [46] based on the leading class-conditional Big GAN [2] and unconditional Style GAN2 [18]. |
| Dataset Splits | Yes | IS and FID are measured using 10k samples; the validation set is used as the reference distribution for FID calculation. [...] IS and FID are measured using 50k samples; the validation set is used as the reference distribution for FID. [...] FID is measured using 50k generated samples; the full training set is used as the reference distribution. |
| Hardware Specification | Yes | Research supported with Cloud TPUs from Google s Tensor Flow Research Cloud (TFRC). |
| Software Dependencies | No | The paper mentions 'Google s Tensor Flow Research Cloud (TFRC)' but does not specify software versions for TensorFlow or any other libraries. |
| Experiment Setup | Yes | For Diff Augment, we adopt Translation + Cutout for the Big GAN models, Color + Cutout for Style GAN2 with 100% data, and Color + Translation + Cutout for Style GAN2 with 10% or 20% data. [...] As Style GAN2 adopts the R1 regularization [27] to stabilize training, we increase its strength from Ξ³ = 0.1 to up to 104 and plot the FID curves in Figure 8b. While we initially ο¬nd that Ξ³ = 0.1 works best under the 100% data setting, the choice of Ξ³ = 103 boosts its performance from 34.05 to 26.87 under the 10% data setting. |