7,800 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
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
Domain Generalization for Activity Recognition via Adaptive Feature Fusion
Human activity recognition requires the efforts to build a generalizable
model using the training datasets with the hope to achieve good performance in
test datasets. However, in real applications, the training and testing datasets
may have totally different distributions due to various reasons such as
different body shapes, acting styles, and habits, damaging the model's
generalization performance. While such a distribution gap can be reduced by
existing domain adaptation approaches, they typically assume that the test data
can be accessed in the training stage, which is not realistic. In this paper,
we consider a more practical and challenging scenario: domain-generalized
activity recognition (DGAR) where the test dataset \emph{cannot} be accessed
during training. To this end, we propose \emph{Adaptive Feature Fusion for
Activity Recognition~(AFFAR)}, a domain generalization approach that learns to
fuse the domain-invariant and domain-specific representations to improve the
model's generalization performance. AFFAR takes the best of both worlds where
domain-invariant representations enhance the transferability across domains and
domain-specific representations leverage the model discrimination power from
each domain. Extensive experiments on three public HAR datasets show its
effectiveness. Furthermore, we apply AFFAR to a real application, i.e., the
diagnosis of Children's Attention Deficit Hyperactivity Disorder~(ADHD), which
also demonstrates the superiority of our approach.Comment: Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST) 2022; Code:
https://github.com/jindongwang/transferlearning/tree/master/code/DeepD
Log-Distributional Approach for Learning Covariate Shift Ratios
Distributional Reinforcement Learning theory suggests that distributional fixed points could play a fundamental role to learning non additive value functions. In particular, we propose a distributional approach for learning Covariate Shift Ratios, whose update rule is originally multiplicative
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