Iterative Assessment and Improvement of DNN Operational Accuracy

• URL: http://arxiv.org/abs/2303.01295v1
• Date: Thu, 2 Mar 2023 14:21:54 GMT
• Title: Iterative Assessment and Improvement of DNN Operational Accuracy
• Authors: Antonio Guerriero, Roberto Pietrantuono, Stefano Russo
• Abstract summary: We propose DAIC (DNN Assessment and Improvement Cycle), an approach which combines ''low-cost'' online pseudo-oracles and ''high-cost'' offline sampling techniques. Preliminary results show the benefits of combining the two approaches.
• Score: 11.447394702830412
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep Neural Networks (DNN) are nowadays largely adopted in many application domains thanks to their human-like, or even superhuman, performance in specific tasks. However, due to unpredictable/unconsidered operating conditions, unexpected failures show up on field, making the performance of a DNN in operation very different from the one estimated prior to release. In the life cycle of DNN systems, the assessment of accuracy is typically addressed in two ways: offline, via sampling of operational inputs, or online, via pseudo-oracles. The former is considered more expensive due to the need for manual labeling of the sampled inputs. The latter is automatic but less accurate. We believe that emerging iterative industrial-strength life cycle models for Machine Learning systems, like MLOps, offer the possibility to leverage inputs observed in operation not only to provide faithful estimates of a DNN accuracy, but also to improve it through remodeling/retraining actions. We propose DAIC (DNN Assessment and Improvement Cycle), an approach which combines ''low-cost'' online pseudo-oracles and ''high-cost'' offline sampling techniques to estimate and improve the operational accuracy of a DNN in the iterations of its life cycle. Preliminary results show the benefits of combining the two approaches and integrating them in the DNN life cycle.

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