7 research outputs found
A review of domain adaptation without target labels
Domain adaptation has become a prominent problem setting in machine learning
and related fields. This review asks the question: how can a classifier learn
from a source domain and generalize to a target domain? We present a
categorization of approaches, divided into, what we refer to as, sample-based,
feature-based and inference-based methods. Sample-based methods focus on
weighting individual observations during training based on their importance to
the target domain. Feature-based methods revolve around on mapping, projecting
and representing features such that a source classifier performs well on the
target domain and inference-based methods incorporate adaptation into the
parameter estimation procedure, for instance through constraints on the
optimization procedure. Additionally, we review a number of conditions that
allow for formulating bounds on the cross-domain generalization error. Our
categorization highlights recurring ideas and raises questions important to
further research.Comment: 20 pages, 5 figure
Graceful Degradation and Related Fields
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