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  1. An autoencoder is a special type of neural network that is trained to copy its input to its output.[1]
  2. For example, given an image of a handwritten digit, an autoencoder first encodes the image into a lower dimensional latent representation, then decodes the latent representation back to an image.[1]
  3. To start, you will train the basic autoencoder using the Fashon MNIST dataset.[1]
  4. An autoencoder can also be trained to remove noise from images.[1]
  5. Autoencoders are an unsupervised learning technique in which we leverage neural networks for the task of representation learning.[2]
  6. An undercomplete autoencoder has no explicit regularization term - we simply train our model according to the reconstruction loss.[2]
  7. For deep autoencoders, we must also be aware of the capacity of our encoder and decoder models.[2]
  8. Sparse autoencoders offer us an alternative method for introducing an information bottleneck without requiring a reduction in the number of nodes at our hidden layers.[2]
  9. An autoencoder neural network is an unsupervised learning algorithm that applies backpropagation, setting the target values to be equal to the inputs.[3]
  10. Recall that \textstyle a^{(2)}_j denotes the activation of hidden unit \textstyle j in the autoencoder.[3]
  11. Having trained a (sparse) autoencoder, we would now like to visualize the function learned by the algorithm, to try to understand what it has learned.[3]
  12. Consider the case of training an autoencoder on \textstyle 10 \times 10 images, so that \textstyle n = 100 .[3]
  13. 오토인코더에 대해 개념과 uncomplete, stacked, denoising, sparse, VAE 오토인코더에 대해 알아보았다.[4]
  14. This article will cover the most common use cases for Autoencoder.[5]
  15. The network architecture for autoencoders can vary between a simple FeedForward network, LSTM network or Convolutional Neural Network depending on the use case.[5]
  16. Let’s say that we have trained an autoencoder on the MNIST dataset.[5]
  17. The code below uses two different images to predict the anomaly score (reconstruction error) using the autoencoder network we trained above.[5]
  18. An autoencoder consists of 3 components: encoder, code and decoder.[6]
  19. To build an autoencoder we need 3 things: an encoding method, decoding method, and a loss function to compare the output with the target.[6]
  20. Lossy: The output of the autoencoder will not be exactly the same as the input, it will be a close but degraded representation.[6]
  21. Unsupervised: To train an autoencoder we don’t need to do anything fancy, just throw the raw input data at it.[6]
  22. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise”.[7]
  23. An autoencoder is a neural network that learns to copy its input to its output.[7]
  24. The k-sparse autoencoder is based on a linear autoencoder (i.e. with linear activation function) and tied weights.[7]
  25. In practice, the objective of denoising autoencoders is that of cleaning the corrupted input, or denoising.[7]
  26. ) Autoencoders are data-specific, which means that they will only be able to compress data similar to what they have been trained on.[8]
  27. Autoencoders are lossy, which means that the decompressed outputs will be degraded compared to the original inputs (similar to MP3 or JPEG compression).[8]
  28. Today two interesting practical applications of autoencoders are data denoising (which we feature later in this post), and dimensionality reduction for data visualization.[8]
  29. As a result, a lot of newcomers to the field absolutely love autoencoders and can't get enough of them.[8]
  30. A variational autoencoder (VAE) provides a probabilistic manner for describing an observation in latent space, such that the encoder describes a probability distribution for each latent attribute.[9]
  31. With this bottleneck structure an autoencoder learns to extract the most important information when the input goes through the latent layers.[9]
  32. Therefore, an autoencoder is an effective way to project data from a high dimension to a lower dimension by extracting the most dominant features and characteristics.[9]
  33. As mentioned earlier, the synthesis of NMR T2 distributions using VAE-NN requires a two-stage training process prior to the testing and deploying the neural network (Fig. 7.2).[9]
  34. For more information on autoencoders and other neural network based approaches, see the work of Geoffrey Hinton (Hinton & Salakhutdinov, 2006)3 and many others.[10]
  35. The autoencoder network has three layers: the input, a hidden layer for encoding, and the output decoding layer.[11]
  36. Autoencoder networks teach themselves how to compress data from the input layer into a shorter code, and then uncompress that code into whatever format best matches the original input.[11]
  37. Denoising autoencoder - Using a partially corrupted input to learn how to recover the original undistorted input.[11]
  38. If you’ve read about unsupervised learning techniques before, you may have come across the term “autoencoder”.[12]
  39. Autoencoders are one of the primary ways that unsupervised learning models are developed.[12]
  40. Briefly, autoencoders operate by taking in data, compressing and encoding the data, and then reconstructing the data from the encoding representation.[12]
  41. Through this process, an autoencoder can learn the important features of the data.[12]
  42. In addition, the autoencoder is explicitly optimized for the data reconstruction from the code.[13]
  43. To avoid overfitting and improve the robustness, Denoising Autoencoder (Vincent et al. 2008) proposed a modification to the basic autoencoder.[13]
  44. Sparse Autoencoder applies a “sparse” constraint on the hidden unit activation to avoid overfitting and improve robustness.[13]
  45. In \(k\)-Sparse Autoencoder (Makhzani and Frey, 2013), the sparsity is enforced by only keeping the top k highest activations in the bottleneck layer with linear activation function.[13]
  46. To analyze such data, several machine learning, bioinformatics, and statistical methods have been applied, among them neural networks such as autoencoders.[14]
  47. In this paper, we investigate several autoencoder architectures that integrate a variety of cancer patient data types (e.g., multi-omics and clinical data).[14]
  48. In this paper we design and systematically analyze several deep-learning approaches for data integration based on Variational Autoencoders (VAEs) (Kingma and Welling, 2014).[14]
  49. Autoencoders learn a compressed representation (embedding/code) of the input data by reconstructing it on the output of the network.[14]
  50. The best performing model was the Composite Model that combined an autoencoder and a future predictor.[15]
  51. 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 # lstm autoencoder recreate sequence from numpy import array from keras .[15]
  52. 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 # lstm autoencoder predict sequence from numpy import array from keras .[15]
  53. 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 # lstm autoencoder reconstruct and predict sequence from numpy import array from keras .[15]
  54. Processing the benchmark dataset MNIST, a deep autoencoder would use binary transformations after each RBM.[16]
  55. The decoding half of a deep autoencoder is a feed-forward net with layers 100, 250, 500 and 1000 nodes wide, respectively.[16]
  56. The decoding half of a deep autoencoder is the part that learns to reconstruct the image.[16]
  57. The scaled word counts are then fed into a deep-belief network, a stack of restricted Boltzmann machines, which themselves are just a subset of feedforward-backprop autoencoders.[16]
  58. Generally, you can consider autoencoders as an unsupervised learning technique, since you don’t need explicit labels to train the model on.[17]
  59. In this tutorial, you’ll learn about autoencoders in deep learning and you will implement a convolutional and denoising autoencoder in Python with Keras.[17]
  60. The compression in autoencoders is achieved by training the network for a period of time and as it learns it tries to best represent the input image at the bottleneck.[17]
  61. You feed an image with just five pixel values into the autoencoder which is compressed by the encoder into three pixel values at the bottleneck (middle layer) or latent space.[17]
  62. Autoencoder is a wildly used deep learning architecture.[18]
  63. Recent studies focused on modifying the autoencoder algorithm to solve the two challenges.[18]
  64. The estimation of the model was done by expectation maximization (EM), but it should be easy for autoencoder to do the job, as pointed out that EM=VAE.[18]
  65. Autoencoder can also be used for supervised learning, similar to principal component regression (PCR).[18]
  66. After the autoencoder was trained on the training set, we obtained the superset outputs for the training and test sets.[19]
  67. In the first part of this tutorial, we’ll discuss what autoencoders are, including how convolutional autoencoders can be applied to image data.[20]
  68. If the goal of an autoencoder is just to reconstruct the input, why even use the network in the first place?[20]
  69. Later in this tutorial, we’ll be training an autoencoder on the MNIST dataset.[20]
  70. Autoencoders cannot generate new, realistic data points that could be considered “passable” by humans.[20]
  71. An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner.[21]
  72. Along with the reduction side, a reconstructing side is also learned, where the autoencoder tries to generate from the reduced encoding a representation as close as possible to its original input.[21]
  73. Denoising autoencoders create a corrupted copy of the input by introducing some noise.[21]
  74. This helps to avoid the autoencoders to copy the input to the output without learning features about the data.[21]
  75. The methodological hierarchy in this work was based on the autoencoder framework, and 12 sampling sizes were considered for landslide susceptibility mapping.[22]
  76. The final prediction results obtained from the autoencoder modeling were evaluated using the testing data set based on qualitative and quantitative analyses to validate the performance of the models.[22]
  77. A flowchart of the proposed autoencoder framework is illustrated in Fig.[22]
  78. The autoencoder is trained to reconstruct input of landslide influencing factors onto the output layer for feature representation, which prevents the simple copying of the data and the network.[22]
  79. For those getting started with neural networks, autoencoders can look and sound intimidating.[23]
  80. First and foremost, autoencoders are trained via unsupervised learning, which means you don't need labels.[23]
  81. An autoencoder is learned to predict its own input by using a noisy version of itself, which forces it to take advantage of structure in the data to learn compact ways of representing it.[23]
  82. To the best of our knowledge, this research is the first to implement stacked autoencoders by using DAEs and AEs for feature learning in DL.[24]
  83. Autoencoders are unsupervised neural networks that use machine learning to do this compression for us.[25]
  84. An autoencoder neural network is an Unsupervised Machine learning algorithm that applies backpropagation, setting the target values to be equal to the inputs.[25]
  85. Autoencoders are used to reduce the size of our inputs into a smaller representation.[25]
  86. So you might be thinking why do we need Autoencoders then?[25]
  87. Configure the VAE to use the specified loss function for the reconstruction, instead of a ReconstructionDistribution.[26]
  88. Note that this is NOT following the standard VAE design (as per Kingma & Welling), which assumes a probabilistic output - i.e., some p(x|z).[26]
  89. Set the number of samples per data point (from VAE state Z) used when doing pretraining.[26]
  90. In this blog we’ve talked about autoencoders several times, both as outliers detection and as dimensionality reduction.[27]
  91. Now, we present another variation of them, variational autoencoder, which makes possible data augmentation.[27]
  92. As a kind reminder, an autoencoder network is composed of a pair of two connected networks: an encoder and a decoder.[27]
  93. But the hidden layer in autoencoders may not be continuous, which might make difficult interpolation.[27]
  94. The generator takes the form of a fully convolutional autoencoder.[28]
  95. The autoencoder (left side of diagram) accepts a masked image as an input, and attempts to reconstruct the original unmasked image.[28]
  96. The discriminator is run using the output of the autoencoder.[28]
  97. The result is used to influence the cost function used to update the autoencoder's weights.[28]
  98. We then use autoencoders to reduce the spectra feature dimensions from 1851 to 10 and re-train the ANN models.[29]
  99. An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra.[29]
  100. We then apply autoencoders to reduce the spectra feature space from 1851 to 10 and re-train ANN models.[29]
  101. The ANN model achieved an average accuracy of 72% and 93% before and after applying the autoencoder, respectively.[29]


  1. 1.0 1.1 1.2 1.3 Intro to Autoencoders
  2. 2.0 2.1 2.2 2.3 Introduction to autoencoders.
  3. 3.0 3.1 3.2 3.3 Unsupervised Feature Learning and Deep Learning Tutorial
  4. 08. 오토인코더 (AutoEncoder)
  5. 5.0 5.1 5.2 5.3 Auto-Encoder: What Is It? And What Is It Used For? (Part 1)
  6. 6.0 6.1 6.2 6.3 Applied Deep Learning - Part 3: Autoencoders
  7. 7.0 7.1 7.2 7.3 Autoencoder
  8. 8.0 8.1 8.2 8.3 Building Autoencoders in Keras
  9. 9.0 9.1 9.2 9.3 Autoencoder - an overview
  10. Autoencoders - an overview
  11. 11.0 11.1 11.2 Autoencoder
  12. 12.0 12.1 12.2 12.3 What is an Autoencoder?
  13. 13.0 13.1 13.2 13.3 From Autoencoder to Beta-VAE
  14. 14.0 14.1 14.2 14.3 Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice
  15. 15.0 15.1 15.2 15.3 A Gentle Introduction to LSTM Autoencoders
  16. 16.0 16.1 16.2 16.3 Deep Autoencoders
  17. 17.0 17.1 17.2 17.3 Keras Autoencoders: Beginner Tutorial
  18. 18.0 18.1 18.2 18.3 Autoencoder in biology — review and perspectives
  19. GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization
  20. 20.0 20.1 20.2 20.3 Autoencoders with Keras, TensorFlow, and Deep Learning
  21. 21.0 21.1 21.2 21.3 Different types of Autoencoders
  22. 22.0 22.1 22.2 22.3 The performance of using an autoencoder for prediction and susceptibility assessment of landslides: A case study on landslides triggered by the 2018 Hokkaido Eastern Iburi earthquake in Japan
  23. 23.0 23.1 23.2 Neural Networks 201: All About Autoencoders
  24. Deep Learning-Based Stacked Denoising and Autoencoder for ECG Heartbeat Classification
  25. 25.0 25.1 25.2 25.3 What are Autoencoders?
  26. 26.0 26.1 26.2 Autoencoders
  27. 27.0 27.1 27.2 27.3 Variational autoencoder as a method of data augmentation
  28. 28.0 28.1 28.2 28.3 Generative Adversarial Denoising Autoencoder for Face Completion
  29. 29.0 29.1 29.2 29.3 An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra