# STRADAViT: Self-Supervised Domain Adaptation of Vision Transformer Backbones for Radio Astronomy

arXiv: https://arxiv.org/abs/2603.29660 · HTML: https://mlsanity.com/paper/2603.29660/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 31 Mar 2026.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Contrastive Learning with Hard Negative Samples](https://mlsanity.com/paper/2010.04592/) (2020)
- [Attention-gating for improved radio galaxy classification](https://mlsanity.com/paper/2012.01248/) (2020)
- [Radio Astronomy in the Era of Vision-Language Models: Prompt Sensitivity and Adaptation](https://mlsanity.com/paper/2509.02615/) (2025)
- [Morphological Classification of Radio Galaxies using Semi-Supervised Group Equivariant CNNs](https://mlsanity.com/paper/2306.00031/) (2023)
- [Self-supervised learning for radio-astronomy source classification: a benchmark](https://mlsanity.com/paper/2411.14078/) (2024)

## Abstract (arXiv)

Next-generation radio astronomy surveys are delivering millions of resolved sources, yet scalable morphology analysis remains difficult across heterogeneous telescopes and imaging pipelines. We present STRADAViT, a self-supervised continued-pretraining framework for learning transferable radio-astronomy encoders from Vision Transformer (ViT) backbones. It combines mixed-survey data curation, radio astronomy-aware training-view generation, and a ViT-MAE-initialized encoder family with optional register tokens. It supports reconstruction-only, contrastive-only, and two-stage branches. Our pretraining dataset comprises 512x512 radio astronomy cutouts drawn from four complementary sources (MeerKAT, ASKAP, LOFAR/LoTSS, and SKA SDC1 simulated data). We evaluate transfer with linear probing (LP) and fine-tuning (FT) on three morphology benchmarks spanning binary and multi-class settings (MiraBest, LoTSS DR2, and Radio Galaxy Zoo). An exploratory three-fold ablation grid guides selection of a register-based two-stage checkpoint using a fixed cross-dataset criterion. Across subsequent 15-seed paired downstream evaluations on fixed partitions, this checkpoint improves linear-probe Macro-F1 over its ViT-MAE initialization on all three benchmarks and improves fine-tuning on MiraBest and RGZ DR1, while LoTSS DR2 fine-tuning declines; all six differences remain statistically supported after Holm correction. A parallel DINOv2 experiment yields mixed adaptation effects: the procedure transfers, but the benefit is not uniform. STRADAViT thus improves frozen ViT representations while retaining clear dataset-dependent limitations and remaining below task-specialized methods on standard MiraBest classification.