# Instance Selection for GANs

arXiv: https://arxiv.org/abs/2007.15255 · HTML: https://mlsanity.com/paper/2007.15255/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2020.

## Built on by (most cited first, 23 of 23)

- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [The Role of ImageNet Classes in Fr'echet Inception Distance](https://mlsanity.com/paper/2203.06026/) (2022)
- [Instance-Conditioned GAN](https://mlsanity.com/paper/2109.05070/) (2021)
- [Generating High Fidelity Data from Low-density Regions using Diffusion Models](https://mlsanity.com/paper/2203.17260/) (2022)
- [GAN-Supervised Dense Visual Alignment](https://mlsanity.com/paper/2112.05143/) (2021)
- [3D generation on ImageNet](https://mlsanity.com/paper/2303.01416/) (2023)
- [Self-Distilled StyleGAN: Towards Generation from Internet Photos](https://mlsanity.com/paper/2202.12211/) (2022)
- [Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective](https://mlsanity.com/paper/2103.00397/) (2021)
- [A Comprehensive Survey on Data-Efficient GANs in Image Generation](https://mlsanity.com/paper/2204.08329/) (2022)
- [Pruning then Reweighting: Towards Data-Efficient Training of Diffusion Models](https://mlsanity.com/paper/2409.19128/) (2024)
- [Manifold Learning Benefits GANs](https://mlsanity.com/paper/2112.12618/) (2021)
- [Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks](https://mlsanity.com/paper/2102.12033/) (2021)
- [Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows](https://mlsanity.com/paper/2305.18910/) (2023)
- [Functional Space Analysis of Local GAN Convergence](https://mlsanity.com/paper/2102.04448/) (2021)
- [Controlled GAN-Based Creature Synthesis via a Challenging Game Art Dataset -- Addressing the Noise-Latent Trade-Off](https://mlsanity.com/paper/2108.08922/) (2021)
- [Generative Adversarial Learning via Kernel Density Discrimination](https://mlsanity.com/paper/2107.06197/) (2021)
- [Fusing Conditional Submodular GAN and Programmatic Weak Supervision](https://mlsanity.com/paper/2312.10366/) (2023)
- [GAIS: A Novel Approach to Instance Selection with Graph Attention Networks](https://mlsanity.com/paper/2412.19201/) (2024)
- [Enhancing Stability in Training Conditional Generative Adversarial Networks via Selective Data Matching](https://mlsanity.com/paper/2107.08792/) (2021)
- [Unrealistic Feature Suppression for Generative Adversarial Networks](https://mlsanity.com/paper/2107.11047/) (2021)
- [infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information](https://mlsanity.com/paper/2305.19344/) (2023)
- [LPA3D: 3D Room-Level Scene Generation from In-the-Wild Images](https://mlsanity.com/paper/2504.02337/) (2025)

## Abstract (arXiv)

Recent advances in Generative Adversarial Networks (GANs) have led to their widespread adoption for the purposes of generating high quality synthetic imagery. While capable of generating photo-realistic images, these models often produce unrealistic samples which fall outside of the data manifold. Several recently proposed techniques attempt to avoid spurious samples, either by rejecting them after generation, or by truncating the model's latent space. While effective, these methods are inefficient, as a large fraction of training time and model capacity are dedicated towards samples that will ultimately go unused. In this work we propose a novel approach to improve sample quality: altering the training dataset via instance selection before model training has taken place. By refining the empirical data distribution before training, we redirect model capacity towards high-density regions, which ultimately improves sample fidelity, lowers model capacity requirements, and significantly reduces training time. Code is available at https://github.com/uoguelph-mlrg/instance_selection_for_gans.