A blood cell classification method based on MAE and active learning
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Autoencoder
Labeled data
Stacked autoencoder is a typical deep neural network. The hidden layers will compress the input data with a better representation than the raw data. Stacked autoencoder has several hidden layers. However, the number of hidden layers is always experiential. In this paper, different hidden layers number autoencoders are discussed. Different depths of stacked autoencoder have different learning capability. The deeper stacked autoencoders have better learning capability which needs more training iterations and time.
Autoencoder
Feature Learning
Representation
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This paper presents a comparison performance on three types of autoencoders, namely, the traditional autoencoder with Restricted Boltzmann Machine (RBM), the stacked autoencoder without RBM and the stacked autoencoder with RBM. The performances are compared based on the reconstruction error for face images and using the same values for the parameters such as the number of neurons in the hidden layers, the training method, and the learning rate. The results show that the RBM stacked autoencoder gives better performance in terms of the reconstruction error compared to the other two architectures.
Autoencoder
Restricted Boltzmann machine
Word error rate
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Autoencoder is an excellent unsupervised learning algorithm. However, it can not generate kinds of sample data in the decoding process. Variational autoencoder is a typical generative adversarial net which can generate various data to augment the sample data. In this paper, we want to do some research about the information learning in hidden layer. In the simulation, we compare the hidden layer learning of hidden layer in conventional autoencoder and variational autoencoder.
Autoencoder
Generative model
Sample (material)
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The anomaly detection technology is the basis for ensuring the safe and stable operation of the on-rail payload. The traditional threshold-based anomaly detection method has low accuracy and poor flexibility, and cannot detect abnormalities in real time. In addition, due to the lack of abnormal samples, the distribution of positive and negative samples is extremely imbalanced, which increases the difficulty of abnormal detection. Therefore, this paper proposes an unsupervised learning method based on AutoEncoder and its variants, the Basic AutoEncoder, Deep AutoEncoder and Sparse AutoEncoder are used to verify the algorithm on three public datasets. And using the above three algorithms to carry out the case application on the real load dataset. The experiments show whether in the public dataset or the real data of the payload, the three methods of AutoEncoder have achieved good results, proving the AutoEncoder and its variants have a good application in anomaly detection. At the same time, it is verified that the three algorithms have different effects on different datasets, which proves that the AutoEncoder with different characteristics need to be selected in different scenarios.
Autoencoder
Payload (computing)
Anomaly (physics)
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Deep Autoencoder has the powerful ability to learn features from large number of unlabeled samples and a small number of labeled samples. In this work, we have improved the network structure of the general deep autoencoder and applied it to the disease auxiliary diagnosis. We have achieved a network by entering the specific indicators and predicting whether suffering from liver disease, the network using real physical examination data for training and verification. Compared with the traditional semi-supervised machine learning algorithm, deep autoencoder will get higher accuracy.
Autoencoder
Training set
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Autoencoder
Transfer of learning
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The stock market prediction problems have received increased attention from researchers due to the high stakes involved and the need for better prediction accuracy. We have developed an architecture by combining a deep autoencoder and long short-term memory to give a novel deep learning framework to forecast the stock price. In stock price forecasting, applying a deep autoencoder that extracts deep features is a new concept. The autoencoder denoise the stock data, and the LSTM model stores past information to predict the future stock price. The deep learning framework that we have used comprises multiple stages. The data is fed into the deep autoencoder to generate a noise-free dataset of the stock price. In the next stage, the deep autoencoder's output is provided as input into the LSTM model to predict the price after n days. Our proposed model could overcome the limitations of traditional machine learning models used in financial prediction. We have validated the model 's effectiveness using multiple datasets and compared the performance with existing models in the literature. The results show that the proposed DAE-LSTM model outperforms the current models.
Autoencoder
Stock Market Prediction
Stock (firearms)
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Autoencoder
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A method for explaining a deep learning model prediction is proposed. It uses a combination of the standard autoencoder and the variational autoencoder. The standard autoencoder is exploited to reconstruct original images and to produce hidden representation vectors. The variational autoencoder is trained to transform the deep learning model outputs (embedding vectors) into the hidden representation vectors of the standard autoencoder. In explaining or testing phase, the variational autoencoder produces a set of vectors based on the explained image embedding. Then the trained decoder part of the standard autoencoder reconstructs a set of images which form a heatmap explaining the original explained image. In fact, the variational autoencoder plays a role of the perturbation technique of images. Numerical experiments with the well-known datasets MNIST and CIFAR10 illustrate the propose method.
Autoencoder
MNIST database
Representation
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On our planet, skin cancer is among the most dangerous diseases. It is, however, difficult to diagnose skin cancer correctly. A variety of tasks have recently been shown to be excelled by machine learning and deep learning algorithms. In the case of skin diseases, these algorithms are very useful. In this article, we examine various machine learning and deep learning techniques and their use in diagnosing skin diseases. In this paper, we discuss common skin diseases and the method of acquiring images from dermatology, and we present several freely available datasets. Our focus shifts to exploring popular machine learning and deep learning architectures and popular frameworks for implementing machine and deep learning algorithms once we have introduced machine learning and deep learning concepts. Following that, performance evaluation metrics are presented. Here we are going to review the literature on machine and deep learning and how these technologies can be used to detect skin diseases. Furthermore, we discuss potential research directions and the challenges in the area. In this paper, the principal goal is to describe contemporary machine learning and deep learning methods for skin disease diagnosis
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