Ctrl-A: Control-Driven Online Data Augmentation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910065483579392 |
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| author | Christensen, Jesper B. Bench, Ciaran Thomas, Spencer A. Aslan, Hüsnü Balslev-Harder, David Smith, Nadia A. S. Manzin, Alessandra |
| author_facet | Christensen, Jesper B. Bench, Ciaran Thomas, Spencer A. Aslan, Hüsnü Balslev-Harder, David Smith, Nadia A. S. Manzin, Alessandra |
| contents | We introduce ControlAugment (Ctrl-A), an automated data augmentation algorithm for image-vision tasks, which incorporates principles from control theory for online adjustment of augmentation strength distributions during model training. Ctrl-A eliminates the need for initialization of individual augmentation strengths. Instead, augmentation strength distributions are dynamically, and individually, adapted during training based on a control-loop architecture and what we define as relative operation response curves. Using an operation-dependent update procedure provides Ctrl-A with the potential to suppress augmentation styles that negatively impact model performance, alleviating the need for manually engineering augmentation policies for new image-vision tasks. Experiments on the CIFAR-10, CIFAR-100, and SVHN-core benchmark datasets using the common WideResNet-28-10 architecture demonstrate that Ctrl-A is highly competitive with existing state-of-the-art data augmentation strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_21819 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Ctrl-A: Control-Driven Online Data Augmentation Christensen, Jesper B. Bench, Ciaran Thomas, Spencer A. Aslan, Hüsnü Balslev-Harder, David Smith, Nadia A. S. Manzin, Alessandra Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Systems and Control We introduce ControlAugment (Ctrl-A), an automated data augmentation algorithm for image-vision tasks, which incorporates principles from control theory for online adjustment of augmentation strength distributions during model training. Ctrl-A eliminates the need for initialization of individual augmentation strengths. Instead, augmentation strength distributions are dynamically, and individually, adapted during training based on a control-loop architecture and what we define as relative operation response curves. Using an operation-dependent update procedure provides Ctrl-A with the potential to suppress augmentation styles that negatively impact model performance, alleviating the need for manually engineering augmentation policies for new image-vision tasks. Experiments on the CIFAR-10, CIFAR-100, and SVHN-core benchmark datasets using the common WideResNet-28-10 architecture demonstrate that Ctrl-A is highly competitive with existing state-of-the-art data augmentation strategies. |
| title | Ctrl-A: Control-Driven Online Data Augmentation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2603.21819 |