{"ID":2836544,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.21652","arxiv_id":"2511.21652","title":"Continual Error Correction on Low-Resource Devices","abstract":"The proliferation of AI models in everyday devices has highlighted a critical challenge: prediction errors that degrade user experience. While existing solutions focus on error detection, they rarely provide efficient correction mechanisms, especially for resource-constrained devices. We present a novel system enabling users to correct AI misclassifications through few-shot learning, requiring minimal computational resources and storage. Our approach combines server-side foundation model training with on-device prototype-based classification, enabling efficient error correction through prototype updates rather than model retraining. The system consists of two key components: (1) a server-side pipeline that leverages knowledge distillation to transfer robust feature representations from foundation models to device-compatible architectures, and (2) a device-side mechanism that enables ultra-efficient error correction through prototype adaptation. We demonstrate our system's effectiveness on both image classification and object detection tasks, achieving over 50% error correction in one-shot scenarios on Food-101 and Flowers-102 datasets while maintaining minimal forgetting (less than 0.02%) and negligible computational overhead. Our implementation, validated through an Android demonstration app, proves the system's practicality in real-world scenarios.","short_abstract":"The proliferation of AI models in everyday devices has highlighted a critical challenge: prediction errors that degrade user experience. While existing solutions focus on error detection, they rarely provide efficient correction mechanisms, especially for resource-constrained devices. We present a novel system enabling...","url_abs":"https://arxiv.org/abs/2511.21652","url_pdf":"https://arxiv.org/pdf/2511.21652v1","authors":"[\"Kirill Paramonov\",\"Mete Ozay\",\"Aristeidis Mystakidis\",\"Nikolaos Tsalikidis\",\"Dimitrios Sotos\",\"Anastasios Drosou\",\"Dimitrios Tzovaras\",\"Hyunjun Kim\",\"Kiseok Chang\",\"Sangdok Mo\",\"Namwoong Kim\",\"Woojong Yoo\",\"Jijoong Moon\",\"Umberto Michieli\"]","published":"2025-11-26T18:24:11Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}