Graceful Degradation and Related Fields
- URL: http://arxiv.org/abs/2106.11119v2
- Date: Thu, 24 Jun 2021 12:30:26 GMT
- Title: Graceful Degradation and Related Fields
- Authors: Jack Dymond
- Abstract summary: graceful degradation refers to the optimisation of model performance as it encounters out-of-distribution data.
This work presents a definition and discussion of graceful degradation and where it can be applied in deployed visual systems.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: When machine learning models encounter data which is out of the distribution
on which they were trained they have a tendency to behave poorly, most
prominently over-confidence in erroneous predictions. Such behaviours will have
disastrous effects on real-world machine learning systems. In this field
graceful degradation refers to the optimisation of model performance as it
encounters this out-of-distribution data. This work presents a definition and
discussion of graceful degradation and where it can be applied in deployed
visual systems. Following this a survey of relevant areas is undertaken,
novelly splitting the graceful degradation problem into active and passive
approaches. In passive approaches, graceful degradation is handled and achieved
by the model in a self-contained manner, in active approaches the model is
updated upon encountering epistemic uncertainties. This work communicates the
importance of the problem and aims to prompt the development of machine
learning strategies that are aware of graceful degradation.
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