About Adaptive AI
Adaptive AI lets you define, refine, and deploy AI models without the traditional AI model training cycle. Instead of spending months collecting data and retraining models, you build concepts directly from examples and improve them continuously through feedback.
No Training Required
Skip the traditional ML workflow entirely. Define what you're looking for using a small number of examples and deploy immediately. No large labeled datasets, no long training cycles, and no waiting for retraining to finish.
Adapts in Real Time
AI that evolves as your understanding evolves. When definitions change or edge cases appear, the system updates instantly through feedback—no retraining, no versioning overhead, no redeployment delays.
Built for Complex, Domain-Specific Problems
Designed for domains where data is scarce, definitions vary, and rare cases matter. Capturing domain knowledge directly from experts, Adaptive AI creates reusable AI capabilities that improve over time.
A Foundation for Scalable AI Systems
By building on foundation models and adaptive learning, Adaptive AI becomes a flexible layer that extends general AI with domain-specific intelligence. Ready to power products, platforms, and AI agents across use cases.
Benchmark Results
Adaptive AI outperforms its closest competitor and traditional few-shot learning methods across all benchmarks, without any training required.
Few-Shot Learning
Top-1 classification accuracy (%). N-WAY K-SHOT: N classes, K example images per class.
Built for few-shot learning — but not limited to it.
While optimized for few-shot learning, Adaptive AI holds its own in fully-supervised settings too, and in some cases even rivals dedicated supervised models. It improves continuously as it sees more examples, learning instantly rather than through a classical training loop.
Fully-Supervised Classification Benchmarks
Top-1 classification accuracy (%) at different training data sizes. Baselines are published models trained on the full dataset using fully supervised training. Adaptive AI requires no training.
References
- [1] O. Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu, and D. Wierstra, "Matching Networks for One Shot Learning," in Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 29, 2016.
- [2] J. Snell, K. Swersky, and R. S. Zemel, "Prototypical Networks for Few-shot Learning," in Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 30, 2017.
- [3] F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. S. Torr, and T. M. Hospedales, "Learning to Compare: Relation Network for Few-Shot Learning," in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), 2018.
- [4] F. Hao, F. He, L. Liu, F. Wu, D. Tao, and J. Cheng, "Class-Aware Patch Embedding Adaptation for Few-Shot Image Classification," in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), 2023.
- [5] L. Tian, J. Feng, X. Chai, W. Chen, L. Wang, X. Liu, and B. Chen, "Prototypes-oriented Transductive Few-shot Learning with Conditional Transport," in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), 2023.
- [6] H. Zhu and P. Koniusz, "Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinement," in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), 2023.
- [7] A. Behera, Z. Wharton, P. Hewage, and A. Bera, "Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification," in Proc. 35th AAAI Conf. Artif. Intell. (AAAI), 2021.
- [8] P. Foret, A. Kleiner, H. Mobahi, and B. Neyshabur, "Sharpness-Aware Minimization for Efficiently Improving Generalization," in Proc. Int. Conf. Learn. Represent. (ICLR), 2021.
- [9] M. Haghighat, P. Moghadam, S. Mohamed, and P. Koniusz, "Pre-training with Random Orthogonal Projection Image Modeling," in Proc. Int. Conf. Learn. Represent. (ICLR), 2024.
- [10] M. Neumann, A. S. Pinto, X. Zhai, and N. Houlsby, "In-domain representation learning for remote sensing," in AI for Earth Sciences Workshop, Int. Conf. Learn. Represent. (ICLR), 2020.
- [11] Y. Tian, J. Zhu, H. Yao, and D. Chen, "Facial Expression Recognition Based on Vision Transformer with Hybrid Local Attention," Applied Sciences, vol. 14, no. 15, p. 6471, 2024.