Context is All You Need
Artificial Neural Networks (ANNs) are increasingly deployed across diverse real-world settings, where they must operate under data distributions that differ from those seen during training. This challenge is central to Domain Generalization (DG), which trains models to generalize to unseen domains without target data, and Test-Time Adaptation (TTA), which improves robustness by adapting to unlabeled test data at deployment. Existing approaches to address these challenges are often complex, resource-intensive, and difficult to scale. We introduce CONTXT (Contextual augmentatiOn for Neural feaTure X Transforms), a simple and intuitive method for contextual adaptation. CONTXT modulates inter...
| Authors | Jean Erik Delanois, Shruti Joshi, Ryan Golden et al. |
|---|---|
| Primary category | cs.LG |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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