# FLARE up your data: Diffusion-based Augmentation Method in Astronomical Imaging

arXiv: https://arxiv.org/abs/2405.13267 · HTML: https://mlsanity.com/paper/2405.13267/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 43 references that are in the map.
- First posted on arXiv 22 May 2024.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [The Curse of Recursion: Training on Generated Data Makes Models Forget](https://mlsanity.com/paper/2305.17493/) (2023)
- [On the detection of synthetic images generated by diffusion models](https://mlsanity.com/paper/2211.00680/) (2022)
- [One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale](https://mlsanity.com/paper/2303.06555/) (2023)
- [Residual Dense Network for Image Restoration](https://mlsanity.com/paper/1812.10477/) (2018)
- [Principles and Practice of Explainable Machine Learning](https://mlsanity.com/paper/2009.11698/) (2020)
- [How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse](https://mlsanity.com/paper/2404.05090/) (2024)
- [Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies](https://mlsanity.com/paper/2012.13736/) (2020)
- [Robust Unsupervised StyleGAN Image Restoration](https://mlsanity.com/paper/2302.06733/) (2023)
- [LSPnet: A 2D Localization-oriented Spacecraft Pose Estimation Neural Network](https://mlsanity.com/paper/2104.09248/) (2021)
- [On enhancing the robustness of Vision Transformers: Defensive Diffusion](https://mlsanity.com/paper/2305.08031/) (2023)
- [A Comparison of Deep Learning Architectures for Optical Galaxy Morphology Classification](https://mlsanity.com/paper/2111.04353/) (2021)
- [Optimizing Brain Tumor Classification: A Comprehensive Study on Transfer Learning and Imbalance Handling in Deep Learning Models](https://mlsanity.com/paper/2308.06821/) (2023)

## Built on by (most cited first, 7 of 7)

- [CosmoCLIP: Generalizing Large Vision-Language Models for Astronomical Imaging](https://mlsanity.com/paper/2407.07315/) (2024)
- [Sample-efficient evidence estimation of score based priors for model selection](https://mlsanity.com/paper/2602.20549/) (2026)
- [AstroSpy: On detecting Fake Images in Astronomy via Joint Image-Spectral Representations](https://mlsanity.com/paper/2407.06817/) (2024)
- [ADAM-Dehaze: Adaptive Density-Aware Multi-Stage Dehazing for Improved Object Detection in Foggy Conditions](https://mlsanity.com/paper/2506.15837/) (2025)
- [Optimizing Diffusion Priors in Image Reconstruction from a Single Observation](https://mlsanity.com/paper/2604.21066/) (2026)
- [AdaptPrompt: Parameter-Efficient Adaptation of VLMs for Generalizable Deepfake Detection](https://mlsanity.com/paper/2512.17730/) (2025)
- [Projected Gradient Unlearning for Text-to-Image Diffusion Models: Defending Against Concept Revival Attacks](https://mlsanity.com/paper/2604.21041/) (2026)

## Abstract (arXiv)

The intersection of Astronomy and AI encounters significant challenges related to issues such as noisy backgrounds, lower resolution (LR), and the intricate process of filtering and archiving images from advanced telescopes like the James Webb. Given the dispersion of raw images in feature space, we have proposed a textittwo-stage augmentation framework entitled as FLARE based on underlinefeature underlinelearning and underlineaugmented underlineresolution underlineenhancement. We first apply lower (LR) to higher resolution (HR) conversion followed by standard augmentations. Secondly, we integrate a diffusion approach to synthetically generate samples using class-concatenated prompts. By merging these two stages using weighted percentiles, we realign the feature space distribution, enabling a classification model to establish a distinct decision boundary and achieve superior generalization on various in-domain and out-of-domain tasks. We conducted experiments on several downstream cosmos datasets and on our optimally distributed SpaceNet dataset across 8-class fine-grained and 4-class macro classification tasks. FLARE attains the highest performance gain of 20.78% for fine-grained tasks compared to similar baselines, while across different classification models, FLARE shows a consistent increment of an average of +15%. This outcome underscores the effectiveness of the FLARE method in enhancing the precision of image classification, ultimately bolstering the reliability of astronomical research outcomes. % Our code and SpaceNet dataset will be released to the public soon. Our code and SpaceNet dataset is available at hrefhttps://github.com/Razaimam45/PlanetXDxbtextithttps://github.com/Razaimam45/PlanetXDxb.