43,443 research outputs found
A Deeper Look at Facial Expression Dataset Bias
Datasets play an important role in the progress of facial expression
recognition algorithms, but they may suffer from obvious biases caused by
different cultures and collection conditions. To look deeper into this bias, we
first conduct comprehensive experiments on dataset recognition and crossdataset
generalization tasks, and for the first time explore the intrinsic causes of
the dataset discrepancy. The results quantitatively verify that current
datasets have a strong buildin bias and corresponding analyses indicate that
the conditional probability distributions between source and target datasets
are different. However, previous researches are mainly based on shallow
features with limited discriminative ability under the assumption that the
conditional distribution remains unchanged across domains. To address these
issues, we further propose a novel deep Emotion-Conditional Adaption Network
(ECAN) to learn domain-invariant and discriminative feature representations,
which can match both the marginal and the conditional distributions across
domains simultaneously. In addition, the largely ignored expression class
distribution bias is also addressed by a learnable re-weighting parameter, so
that the training and testing domains can share similar class distribution.
Extensive cross-database experiments on both lab-controlled datasets (CK+,
JAFFE, MMI and Oulu-CASIA) and real-world databases (AffectNet, FER2013, RAF-DB
2.0 and SFEW 2.0) demonstrate that our ECAN can yield competitive performances
across various facial expression transfer tasks and outperform the
state-of-theart methods
Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges
Machine learning has evolved into an enabling technology for a wide range of
highly successful applications. The potential for this success to continue and
accelerate has placed machine learning (ML) at the top of research, economic
and political agendas. Such unprecedented interest is fuelled by a vision of ML
applicability extending to healthcare, transportation, defence and other
domains of great societal importance. Achieving this vision requires the use of
ML in safety-critical applications that demand levels of assurance beyond those
needed for current ML applications. Our paper provides a comprehensive survey
of the state-of-the-art in the assurance of ML, i.e. in the generation of
evidence that ML is sufficiently safe for its intended use. The survey covers
the methods capable of providing such evidence at different stages of the
machine learning lifecycle, i.e. of the complex, iterative process that starts
with the collection of the data used to train an ML component for a system, and
ends with the deployment of that component within the system. The paper begins
with a systematic presentation of the ML lifecycle and its stages. We then
define assurance desiderata for each stage, review existing methods that
contribute to achieving these desiderata, and identify open challenges that
require further research
Transfer Learning for Thermal Comfort Prediction in Multiple Cities
HVAC (Heating, Ventilation and Air Conditioning) system is an important part
of a building, which constitutes up to 40% of building energy usage. The main
purpose of HVAC, maintaining appropriate thermal comfort, is crucial for the
best utilisation of energy usage. Besides, thermal comfort is also crucial for
well-being, health, and work productivity. Recently, data-driven thermal
comfort models have got better performance than traditional knowledge-based
methods (e.g. Predicted Mean Vote Model). An accurate thermal comfort model
requires a large amount of self-reported thermal comfort data from indoor
occupants which undoubtedly remains a challenge for researchers. In this
research, we aim to tackle this data-shortage problem and boost the performance
of thermal comfort prediction. We utilise sensor data from multiple cities in
the same climate zone to learn thermal comfort patterns. We present a transfer
learning based multilayer perceptron model from the same climate zone
(TL-MLP-C*) for accurate thermal comfort prediction. Extensive experimental
results on ASHRAE RP-884, the Scales Project and Medium US Office datasets show
that the performance of the proposed TL-MLP-C* exceeds the state-of-the-art
methods in accuracy, precision and F1-score
Evaluation of Transfer Learning for Classification of: (1) Diabetic Retinopathy by Digital Fundus Photography and (2) Diabetic Macular Edema, Choroidal Neovascularization and Drusen by Optical Coherence Tomography
Deep learning has been successfully applied to a variety of image
classification tasks. There has been keen interest to apply deep learning in
the medical domain, particularly specialties that heavily utilize imaging, such
as ophthalmology. One issue that may hinder application of deep learning to the
medical domain is the vast amount of data necessary to train deep neural
networks (DNNs). Because of regulatory and privacy issues associated with
medicine, and the generally proprietary nature of data in medical domains,
obtaining large datasets to train DNNs is a challenge, particularly in the
ophthalmology domain.
Transfer learning is a technique developed to address the issue of applying
DNNs for domains with limited data. Prior reports on transfer learning have
examined custom networks to fully train or used a particular DNN for transfer
learning. However, to the best of my knowledge, no work has systematically
examined a suite of DNNs for transfer learning for classification of diabetic
retinopathy, diabetic macular edema, and two key features of age-related
macular degeneration. This work attempts to investigate transfer learning for
classification of these ophthalmic conditions. Part I gives a condensed
overview of neural networks and the DNNs under evaluation. Part II gives the
reader the necessary background concerning diabetic retinopathy and prior work
on classification using retinal fundus photographs. The methodology and results
of transfer learning for diabetic retinopathy classification are presented,
showing that transfer learning towards this domain is feasible, with promising
accuracy. Part III gives an overview of diabetic macular edema, choroidal
neovascularization and drusen (features associated with age-related macular
degeneration), and presents results for transfer learning evaluation using
optical coherence tomography to classify these entities
Legal Area Classification: A Comparative Study of Text Classifiers on Singapore Supreme Court Judgments
This paper conducts a comparative study on the performance of various machine
learning (``ML'') approaches for classifying judgments into legal areas. Using
a novel dataset of 6,227 Singapore Supreme Court judgments, we investigate how
state-of-the-art NLP methods compare against traditional statistical models
when applied to a legal corpus that comprised few but lengthy documents. All
approaches tested, including topic model, word embedding, and language
model-based classifiers, performed well with as little as a few hundred
judgments. However, more work needs to be done to optimize state-of-the-art
methods for the legal domain.Comment: Accepted to the 1st Workshop on Natural Legal Language Processing
(co-located with NAACL2019
A Many Objective Optimization Approach for Transfer Learning in EEG Classification
In Brain-Computer Interfacing (BCI), due to inter-subject non-stationarities
of electroencephalogram (EEG), classifiers are trained and tested using EEG
from the same subject. When physical disabilities bottleneck the natural
modality of performing a task, acquisition of ample training data is difficult
which practically obstructs classifier training. Previous works have tackled
this problem by generalizing the feature space amongst multiple subjects
including the test subject. This work aims at knowledge transfer to classify
EEG of the target subject using a classifier trained with the EEG of another
unit source subject. A many-objective optimization framework is proposed where
optimal weights are obtained for projecting features in another dimension such
that single source-trained target EEG classification performance is maximized
with the modified features. To validate the approach, motor imagery tasks from
the BCI Competition III Dataset IVa are classified using power spectral density
based features and linear support vector machine. Several performance metrics,
improvement in accuracy, sensitivity to the dimension of the projected space,
assess the efficacy of the proposed approach. Addressing single-source training
promotes independent living of differently-abled individuals by reducing
assistance from others. The proposed approach eliminates the requirement of EEG
from multiple source subjects and is applicable to any existing feature
extractors and classifiers. Source code is available at
http://worksupplements.droppages.com/tlbci.html.Comment: Pre-submission wor
Synthetic Examples Improve Generalization for Rare Classes
The ability to detect and classify rare occurrences in images has important
applications - for example, counting rare and endangered species when studying
biodiversity, or detecting infrequent traffic scenarios that pose a danger to
self-driving cars. Few-shot learning is an open problem: current computer
vision systems struggle to categorize objects they have seen only rarely during
training, and collecting a sufficient number of training examples of rare
events is often challenging and expensive, and sometimes outright impossible.
We explore in depth an approach to this problem: complementing the few
available training images with ad-hoc simulated data.
Our testbed is animal species classification, which has a real-world
long-tailed distribution. We analyze the effect of different axes of variation
in simulation, such as pose, lighting, model, and simulation method, and we
prescribe best practices for efficiently incorporating simulated data for
real-world performance gain. Our experiments reveal that synthetic data can
considerably reduce error rates for classes that are rare, that as the amount
of simulated data is increased, accuracy on the target class improves, and that
high variation of simulated data provides maximum performance gain
Multitask Learning for Large-scale Semantic Change Detection
Change detection is one of the main problems in remote sensing, and is
essential to the accurate processing and understanding of the large scale Earth
observation data available through programs such as Sentinel and Landsat. Most
of the recently proposed change detection methods bring deep learning to this
context, but openly available change detection datasets are still very scarce,
which limits the methods that can be proposed and tested. In this paper we
present the first large scale high resolution semantic change detection (HRSCD)
dataset, which enables the usage of deep learning methods for semantic change
detection. The dataset contains coregistered RGB image pairs, pixel-wise change
information and land cover information. We then propose several methods using
fully convolutional neural networks to perform semantic change detection. Most
notably, we present a network architecture that performs change detection and
land cover mapping simultaneously, while using the predicted land cover
information to help to predict changes. We also describe a sequential training
scheme that allows this network to be trained without setting a hyperparameter
that balances different loss functions and achieves the best overall results.Comment: Preprint submitted to Computer Vision and Image Understandin
A Survey of Deep Facial Attribute Analysis
Facial attribute analysis has received considerable attention when deep
learning techniques made remarkable breakthroughs in this field over the past
few years. Deep learning based facial attribute analysis consists of two basic
sub-issues: facial attribute estimation (FAE), which recognizes whether facial
attributes are present in given images, and facial attribute manipulation
(FAM), which synthesizes or removes desired facial attributes. In this paper,
we provide a comprehensive survey of deep facial attribute analysis from the
perspectives of both estimation and manipulation. First, we summarize a general
pipeline that deep facial attribute analysis follows, which comprises two
stages: data preprocessing and model construction. Additionally, we introduce
the underlying theories of this two-stage pipeline for both FAE and FAM.
Second, the datasets and performance metrics commonly used in facial attribute
analysis are presented. Third, we create a taxonomy of state-of-the-art methods
and review deep FAE and FAM algorithms in detail. Furthermore, several
additional facial attribute related issues are introduced, as well as relevant
real-world applications. Finally, we discuss possible challenges and promising
future research directions.Comment: submitted to International Journal of Computer Vision (IJCV
Bellwethers: A Baseline Method For Transfer Learning
Software analytics builds quality prediction models for software projects.
Experience shows that (a) the more projects studied, the more varied are the
conclusions; and (b) project managers lose faith in the results of software
analytics if those results keep changing. To reduce this conclusion
instability, we propose the use of "bellwethers": given N projects from a
community the bellwether is the project whose data yields the best predictions
on all others. The bellwethers offer a way to mitigate conclusion instability
because conclusions about a community are stable as long as this bellwether
continues as the best oracle. Bellwethers are also simple to discover (just
wrap a for-loop around standard data miners). When compared to other transfer
learning methods (TCA+, transfer Naive Bayes, value cognitive boosting), using
just the bellwether data to construct a simple transfer learner yields
comparable predictions. Further, bellwethers appear in many SE tasks such as
defect prediction, effort estimation, and bad smell detection. We hence
recommend using bellwethers as a baseline method for transfer learning against
which future work should be comparedComment: 23 Page
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