20,977 research outputs found
Resisting Large Data Variations via Introspective Transformation Network
Training deep networks that generalize to a wide range of variations in test
data is essential to building accurate and robust image classifiers. One
standard strategy is to apply data augmentation to synthetically enlarge the
training set. However, data augmentation is essentially a brute-force method
which generates uniform samples from some pre-defined set of transformations.
In this paper, we propose a principled approach to train networks with
significantly improved resistance to large variations between training and
testing data. This is achieved by embedding a learnable transformation module
into the introspective network, which is a convolutional neural network (CNN)
classifier empowered with generative capabilities. Our approach alternates
between synthesizing pseudo-negative samples and transformed positive examples
based on the current model, and optimizing model predictions on these
synthesized samples. Experimental results verify that our approach
significantly improves the ability of deep networks to resist large variations
between training and testing data and achieves classification accuracy
improvements on several benchmark datasets, including MNIST, affNIST, SVHN,
CIFAR-10 and miniImageNet.Comment: camera-ready version, WACV 202
Tongue image constitution recognition based on Complexity Perception method
Background and Object: In China, body constitution is highly related to
physiological and pathological functions of human body and determines the
tendency of the disease, which is of great importance for treatment in clinical
medicine. Tongue diagnosis, as a key part of Traditional Chinese Medicine
inspection, is an important way to recognize the type of constitution.In order
to deploy tongue image constitution recognition system on non-invasive mobile
device to achieve fast, efficient and accurate constitution recognition, an
efficient method is required to deal with the challenge of this kind of complex
environment. Methods: In this work, we perform the tongue area detection,
tongue area calibration and constitution classification using methods which are
based on deep convolutional neural network. Subject to the variation of
inconstant environmental condition, the distribution of the picture is uneven,
which has a bad effect on classification performance. To solve this problem, we
propose a method based on the complexity of individual instances to divide
dataset into two subsets and classify them separately, which is capable of
improving classification accuracy. To evaluate the performance of our proposed
method, we conduct experiments on three sizes of tongue datasets, in which deep
convolutional neural network method and traditional digital image analysis
method are respectively applied to extract features for tongue images. The
proposed method is combined with the base classifier Softmax, SVM, and
DecisionTree respectively. Results: As the experiments results shown, our
proposed method improves the classification accuracy by 1.135% on average and
achieves 59.99% constitution classification accuracy. Conclusions: Experimental
results on three datasets show that our proposed method can effectively improve
the classification accuracy of tongue constitution recognition
Modified Diversity of Class Probability Estimation Co-training for Hyperspectral Image Classification
Due to the limited amount and imbalanced classes of labeled training data,
the conventional supervised learning can not ensure the discrimination of the
learned feature for hyperspectral image (HSI) classification. In this paper, we
propose a modified diversity of class probability estimation (MDCPE) with two
deep neural networks to learn spectral-spatial feature for HSI classification.
In co-training phase, recurrent neural network (RNN) and convolutional neural
network (CNN) are utilized as two learners to extract features from labeled and
unlabeled data. Based on the extracted features, MDCPE selects most credible
samples to update initial labeled data by combining k-means clustering with the
traditional diversity of class probability estimation (DCPE) co-training. In
this way, MDCPE can keep new labeled data class-balanced and extract
discriminative features for both the minority and majority classes. During
testing process, classification results are acquired by co-decision of the two
learners. Experimental results demonstrate that the proposed semi-supervised
co-training method can make full use of unlabeled information to enhance
generality of the learners and achieve favorable accuracies on all three widely
used data sets: Salinas, Pavia University and Pavia Center.Comment: 13 pages, 10 figures and 8 table
Offline and Online Deep Learning for Image Recognition
Image recognition using Deep Learning has been evolved for decades though
advances in the field through different settings is still a challenge. In this
paper, we present our findings in searching for better image classifiers in
offline and online environments. We resort to Convolutional Neural Network and
its variations of fully connected Multi-layer Perceptron. Though still
preliminary, these results are encouraging and may provide a better
understanding about the field and directions toward future works.Comment: 5 page
p-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning
Recent advances in learning Deep Neural Network (DNN) architectures have
received a great deal of attention due to their ability to outperform
state-of-the-art classifiers across a wide range of applications, with little
or no feature engineering. In this paper, we broadly study the applicability of
deep learning to website fingerprinting. We show that unsupervised DNNs can be
used to extract low-dimensional feature vectors that improve the performance of
state-of-the-art website fingerprinting attacks. When used as classifiers, we
show that they can match or exceed performance of existing attacks across a
range of application scenarios, including fingerprinting Tor website traces,
fingerprinting search engine queries over Tor, defeating fingerprinting
defenses, and fingerprinting TLS-encrypted websites. Finally, we show that DNNs
can be used to predict the fingerprintability of a website based on its
contents, achieving 99% accuracy on a data set of 4500 website downloads.Comment: Under submissio
Dense Adaptive Cascade Forest: A Self Adaptive Deep Ensemble for Classification Problems
Recent researches have shown that deep forest ensemble achieves a
considerable increase in classification accuracy compared with the general
ensemble learning methods, especially when the training set is small. In this
paper, we take advantage of deep forest ensemble and introduce the Dense
Adaptive Cascade Forest (daForest). Our model has a better performance than the
original Cascade Forest with three major features: first, we apply SAMME.R
boosting algorithm to improve the performance of the model. It guarantees the
improvement as the number of layers increases. Second, our model connects each
layer to the subsequent ones in a feed-forward fashion, which enhances the
capability of the model to resist performance degeneration. Third, we add a
hyper-parameters optimization layer before the first classification layer,
making our model spend less time to set up and find the optimal
hyper-parameters. Experimental results show that daForest performs
significantly well, and in some cases, even outperforms neural networks and
achieves state-of-the-art results.Comment: 22 pages, 6 figure
Surface Defect Classification in Real-Time Using Convolutional Neural Networks
Surface inspection systems are an important application domain for computer
vision, as they are used for defect detection and classification in the
manufacturing industry. Existing systems use hand-crafted features which
require extensive domain knowledge to create. Even though Convolutional neural
networks (CNNs) have proven successful in many large-scale challenges,
industrial inspection systems have yet barely realized their potential due to
two significant challenges: real-time processing speed requirements and
specialized narrow domain-specific datasets which are sometimes limited in
size. In this paper, we propose CNN models that are specifically designed to
handle capacity and real-time speed requirements of surface inspection systems.
To train and evaluate our network models, we created a surface image dataset
containing more than 22000 labeled images with many types of surface materials
and achieved 98.0% accuracy in binary defect classification. To solve the class
imbalance problem in our datasets, we introduce neural data augmentation
methods which are also applicable to similar domains that suffer from the same
problem. Our results show that deep learning based methods are feasible to be
used in surface inspection systems and outperform traditional methods in
accuracy and inference time by considerable margins.Comment: Supplementary material will follo
Residual-CNDS for Grand Challenge Scene Dataset
Increasing depth of convolutional neural networks (CNNs) is a highly
promising method of increasing the accuracy of the (CNNs). Increased CNN depth
will also result in increased layer count (parameters), leading to a slow
backpropagation convergence prone to overfitting. We trained our model
(Residual-CNDS) to classify very large-scale scene datasets MIT Places 205, and
MIT Places 365-Standard. The outcome result from the two datasets proved our
proposed model (Residual-CNDS) effectively handled the slow convergence,
overfitting, and degradation. CNNs that include deep supervision (CNDS) add
supplementary branches to the deep convolutional neural network in specified
layers by calculating vanishing, effectively addressing delayed convergence and
overfitting. Nevertheless, (CNDS) does not resolve degradation; hence, we add
residual learning to the (CNDS) in certain layers after studying the best place
in which to add it. With this approach we overcome degradation in the very deep
network. We have built two models (Residual-CNDS 8), and (Residual-CNDS 10).
Moreover, we tested our models on two large-scale datasets, and we compared our
results with other recently introduced cutting-edge networks in the domain of
top-1 and top-5 classification accuracy. As a result, both of models have shown
good improvement, which supports the assertion that the addition of residual
connections enhances network CNDS accuracy without adding any computation
complexity
Real-Time Document Image Classification using Deep CNN and Extreme Learning Machines
This paper presents an approach for real-time training and testing for
document image classification. In production environments, it is crucial to
perform accurate and (time-)efficient training. Existing deep learning
approaches for classifying documents do not meet these requirements, as they
require much time for training and fine-tuning the deep architectures.
Motivated from Computer Vision, we propose a two-stage approach. The first
stage trains a deep network that works as feature extractor and in the second
stage, Extreme Learning Machines (ELMs) are used for classification. The
proposed approach outperforms all previously reported structural and deep
learning based methods with a final accuracy of 83.24% on Tobacco-3482 dataset,
leading to a relative error reduction of 25% when compared to a previous
Convolutional Neural Network (CNN) based approach (DeepDocClassifier). More
importantly, the training time of the ELM is only 1.176 seconds and the overall
prediction time for 2,482 images is 3.066 seconds. As such, this novel approach
makes deep learning-based document classification suitable for large-scale
real-time applications
A Hybrid Deep Learning Approach for Texture Analysis
Texture classification is a problem that has various applications such as
remote sensing and forest species recognition. Solutions tend to be custom fit
to the dataset used but fails to generalize. The Convolutional Neural Network
(CNN) in combination with Support Vector Machine (SVM) form a robust selection
between powerful invariant feature extractor and accurate classifier. The
fusion of experts provides stability in classification rates among different
datasets
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