5,442 research outputs found
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
Incremental multi-domain learning with network latent tensor factorization
The prominence of deep learning, large amount of annotated data and
increasingly powerful hardware made it possible to reach remarkable performance
for supervised classification tasks, in many cases saturating the training
sets. However the resulting models are specialized to a single very specific
task and domain. Adapting the learned classification to new domains is a hard
problem due to at least three reasons: (1) the new domains and the tasks might
be drastically different; (2) there might be very limited amount of annotated
data on the new domain and (3) full training of a new model for each new task
is prohibitive in terms of computation and memory, due to the sheer number of
parameters of deep CNNs. In this paper, we present a method to learn
new-domains and tasks incrementally, building on prior knowledge from already
learned tasks and without catastrophic forgetting. We do so by jointly
parametrizing weights across layers using low-rank Tucker structure. The core
is task agnostic while a set of task specific factors are learnt on each new
domain. We show that leveraging tensor structure enables better performance
than simply using matrix operations. Joint tensor modelling also naturally
leverages correlations across different layers. Compared with previous methods
which have focused on adapting each layer separately, our approach results in
more compact representations for each new task/domain. We apply the proposed
method to the 10 datasets of the Visual Decathlon Challenge and show that our
method offers on average about 7.5x reduction in number of parameters and
competitive performance in terms of both classification accuracy and Decathlon
score.Comment: AAAI2
H2 Optimal Coordination of Homogeneous Agents Subject to Limited Information Exchange
Controllers with a diagonal-plus-low-rank structure constitute a scalable
class of controllers for multi-agent systems. Previous research has shown that
diagonal-plus-low-rank control laws appear as the optimal solution to a class
of multi-agent H2 coordination problems, which arise in the control of wind
farms. In this paper we show that this result extends to the case where the
information exchange between agents is subject to limitations. We also show
that the computational effort required to obtain the optimal controller is
independent of the number of agents and provide analytical expressions that
quantify the usefulness of information exchange
- …