676,725 research outputs found
Transfer learning for radio galaxy classification
In the context of radio galaxy classification, most state-of-the-art neural
network algorithms have been focused on single survey data. The question of
whether these trained algorithms have cross-survey identification ability or
can be adapted to develop classification networks for future surveys is still
unclear. One possible solution to address this issue is transfer learning,
which re-uses elements of existing machine learning models for different
applications. Here we present radio galaxy classification based on a 13-layer
Deep Convolutional Neural Network (DCNN) using transfer learning methods
between different radio surveys. We find that our machine learning models
trained from a random initialization achieve accuracies comparable to those
found elsewhere in the literature. When using transfer learning methods, we
find that inheriting model weights pre-trained on FIRST images can boost model
performance when re-training on lower resolution NVSS data, but that inheriting
pre-trained model weights from NVSS and re-training on FIRST data impairs the
performance of the classifier. We consider the implication of these results in
the context of future radio surveys planned for next-generation radio
telescopes such as ASKAP, MeerKAT, and SKA1-MID
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
Recommended from our members
Agents Teaching Agents: A Survey on Inter-agent Transfer Learning
Autonomous Agents and Multi-Agent Systems published a piece about the Inter-agent Transfer Learning in January 2020.Office of the VP for Researc
Adversarial Attack and Defense on Graph Data: A Survey
Deep neural networks (DNNs) have been widely applied to various applications
including image classification, text generation, audio recognition, and graph
data analysis. However, recent studies have shown that DNNs are vulnerable to
adversarial attacks. Though there are several works studying adversarial attack
and defense strategies on domains such as images and natural language
processing, it is still difficult to directly transfer the learned knowledge to
graph structure data due to its representation challenges. Given the importance
of graph analysis, an increasing number of works start to analyze the
robustness of machine learning models on graph data. Nevertheless, current
studies considering adversarial behaviors on graph data usually focus on
specific types of attacks with certain assumptions. In addition, each work
proposes its own mathematical formulation which makes the comparison among
different methods difficult. Therefore, in this paper, we aim to survey
existing adversarial learning strategies on graph data and first provide a
unified formulation for adversarial learning on graph data which covers most
adversarial learning studies on graph. Moreover, we also compare different
attacks and defenses on graph data and discuss their corresponding
contributions and limitations. In this work, we systemically organize the
considered works based on the features of each topic. This survey not only
serves as a reference for the research community, but also brings a clear image
researchers outside this research domain. Besides, we also create an online
resource and keep updating the relevant papers during the last two years. More
details of the comparisons of various studies based on this survey are
open-sourced at
https://github.com/YingtongDou/graph-adversarial-learning-literature.Comment: In submission to Journal. For more open-source and up-to-date
information, please check our Github repository:
https://github.com/YingtongDou/graph-adversarial-learning-literatur
Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms
The advent of federated learning has facilitated large-scale data exchange
amongst machine learning models while maintaining privacy. Despite its brief
history, federated learning is rapidly evolving to make wider use more
practical. One of the most significant advancements in this domain is the
incorporation of transfer learning into federated learning, which overcomes
fundamental constraints of primary federated learning, particularly in terms of
security. This chapter performs a comprehensive survey on the intersection of
federated and transfer learning from a security point of view. The main goal of
this study is to uncover potential vulnerabilities and defense mechanisms that
might compromise the privacy and performance of systems that use federated and
transfer learning.Comment: Accepted for publication in edited book titled "Federated and
Transfer Learning", Springer, Cha
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