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Introduction to Autoencoders: Types and Applications
The document provides a comprehensive introduction to autoencoders, explaining their architecture, types, and various applications in unsupervised learning, including dimensionality reduction, feature learning, and anomaly detection. It details how autoencoders function through an encoder that compresses input data into a latent space and a decoder that reconstructs the original input, as well as comparing autoencoders to other techniques like GANs and PCA. Additionally, it outlines the strengths and weaknesses of autoencoders, emphasizing their role in generating new data and handling complex data relationships.
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Introduction to Autoencoders: Types and Applications
- 1. Introduction to autoencoder Prepared by: Dr. Amr Rashed
- 3. Agenda • Introduction toAutoencoders • What Are Autoencoders? • How Autoencoders Achieve High-Quality Reconstructions? • Revisiting the Story • Types of Autoencoder • Vanilla Autoencoder • Convolutional Autoencoder (CAE) • Denoising Autoencoder • Sparse Autoencoder • Variational Autoencoder (VAE) • Sequence-to-Sequence Autoencoder
- 4. Cont . • WhatAre the Applications of Autoencoders? • Dimensionality Reduction • Feature Learning • Anomaly Detection • Denoising Images • Image Inpainting • Generative Modeling • Recommender Systems • Sequence-to-Sequence Learning • Image Segmentation • How Are Autoencoders Different from GANs? • Architecture • Training Process • Objectives
- 5. Introduction to autoencoder •To develop an understanding of Autoencoders and the problem they aim to solve, let’s consider • Story set in the city of Fashionopolis. • Clothing items organized in a wide, tall closet. • Fashion consultant Alex proposes the strategy. • Requesting specific items by informing Alex of their position. • Fine-tuning the closet's arrangement for accuracy. • Alex can reproduce clothing items impeccably. • Possibility of creating brand-new clothing items in empty spots. • Infinite possibilities for generating novel clothing.
- 6. What Are Autoencode rs? • Anautoencoder is an artificial neural network used for unsupervised learning tasks (i.e., no class labels or labeled data) such as dimensionality reduction, feature extraction, and data compression. They seek to: • Accept an input set of data (i.e., the input) • Internally compress the input data into a latent space representation (i.e., a single vector that compresses and quantifies the input) • Reconstruct the input data from this latent representation (i.e., the output)
- 7. Cont. purposes to discoverunderlying correlations among data and represent data in a smaller dimension . The autoencoders frame unsupervised learning problems as supervised learning problems to train a neural network model . The input only is passed at the output. The input is squeezed down o a lower encoded representation using an encoder network, then a decoder network decodes the encoding to recreate back the input . The encoding produced by the encoder layer has a lower-dimensional representation of the data and shows several interesting complex relationships among data .
- 8. Cont. An Autoencoder hasthe following parts: 1. Encoder: The encoder is the part of the network that takes in the input and produces a lower-dimensional encoding 2. Bottleneck: It is the lower dimensional hidden layer where the encoding is produced. The bottleneck layer has a lower number of nodes and the number of nodes in the bottleneck layer also gives the dimension of the encoding of the input. 3. Decoder: The decoder takes in the encoding and recreates back the input.
- 9. Cont. The bottleneck layeris the lower dimension layer. In the diagram, we have the neural networks encoder and decoder. Phi and Theta are the representing parameters of the encoder and decoder respectively . The target of this model is such that the Input is equivalent to the Reconstructed Output . To achieve this we minimize a loss function named Reconstruction Loss . Basically, Reconstruction Loss is given by the error between the input and the reconstructed output . It is usually given by the Mean Square error or Binary Cross entropy between the input and reconstructed output . Binary Cross entropy is used if the data is binary .
- 10. Cont. • An autoencoderconsists of the following two primary components: • Encoder: The encoder compresses input data into a lower-dimensional representation known as the latent space or code. This latent space, often called embedding, aims to retain as much information as possible, allowing the decoder to reconstruct the data with high precision. If we denote our input data as x and the encoder as E, then the output latent space representation, s, would be s=E(x). • Decoder: The decoder reconstructs the original input data by accepting the latent space representation s. If we denote the decoder function as D and the output of the detector as o, then we can represent the decoder as o = D(s). • Both encoder and decoder are typically composed of one or more layers, which can be fully connected, convolutional, or recurrent, depending on the input data’s nature and the autoencoder’s architecture.
- 13. Revisiting the Story • Withthe story analogy fresh in our minds, let’s connect it to the technical concept of Autoencoders. In the story, you act as the encoder, organizing each clothing item into a specific location within the wardrobe and assigning an encoding. • Meanwhile, your friend Alex takes on the role of the decoder, selecting a location in the wardrobe and attempting to recreate (or, in technical terms) the clothing item (a process referred to as decoding).
- 14. Autoencoder vs PCA Wehave to keep in mind that the reason behind using an autoencoder is that we want to understand and represent only the deep correlations and relationships among data . We need a generalized lower-dimensional representation . That is why, if the features of the data are not correlated at all then it is hard for an autoencoder to represent the data in a lower dimension . If while designing the neural network, we use a very large number of nodes in the bottleneck layer, it will create a large dimensional encoding . The problem that exists here is, the network might cheat and overfit the input data by simply remembering the input data . In this case, we will not be able to get the correct relationships in our encodings. Again, if we use a shallow network with a very small number of nodes, it will be very hard to capture all the relationships. So, we must be very careful during designing the network .
- 15. Cont. • PCA orprincipal component analysis tries to find lower-dimensional orthogonal hyperplanes that describe the original data by capturing the maximum possible variance in the data and the important correlations consequently. • We need to focus on the fact that we are talking about finding a hyperplane, so it's linear. But often correlations are non-linear, which are not covered by PCA. • As we can see in the above diagram autoencoders cover non-linear data dependencies and, thus are a better way than PCA for dimensionality reduction.
- 16. • Note: Infact, if we were to construct a linear network (ie. without the use of nonlinear activation functions at each layer) we would observe a similar dimensionality reduction as observed in PCA. See Geoffrey Hinton's discussion of this here.
- 17. Types of Autoencoder • Thereare several types of autoencoders, each with its unique properties and use cases. • 1-Vanilla Autoencoder • Figure 4 shows the simplest form of an autoencoder, consisting of one or more fully connected layers for both the encoder and decoder. It works well for simple data but may struggle with complex patterns.
- 18. 2-Convolutional Autoencoder (CAE) •Utilizes convolutional layers in both the encoder and decoder, making it suitable for handling image data. By exploiting the spatial information in images, CAEs can capture complex patterns and structures more effectively than vanilla autoencoders and accomplish tasks such as image segmentation, as shown in Figure 5.
- 19. 3-Denoising Autoencoder • Thisautoencoder is designed to remove noise from corrupted input data, as shown in Figure 6. During training, the input data is intentionally corrupted by adding noise, while the target remains the original, uncorrupted data. • The autoencoder learns to reconstruct the clean data from the noisy input, making it useful for image denoising and data preprocessing tasks.
- 20. 4-Sparse Autoencoder • Thistype of autoencoder enforces sparsity in the latent space representation by adding a sparsity constraint to the loss function (as shown in Figure 7). This constraint encourages the autoencoder to represent the input data using a small number of active neurons in the latent space, resulting in more efficient and robust feature extraction.
- 21. 5-Variational Autoencoder (VAE) •Figure 8 shows a generative model that introduces a probabilistic layer in the latent space, allowing for sampling and generation of new data. • VAEs can generate new samples from the learned latent distribution, making them ideal for image generation and style transfer tasks.
- 22. 6-Sequence-to-Sequence Autoencoder • Also knownas a Recurrent Autoencoder, this type of autoencoder utilizes recurrent neural network (RNN) layers (e.g., long short-term memory (LSTM) or gated recurrent unit (GRU)) in both the encoder and decoder shown in Figure 9. • This architecture is well-suited for handling sequential data (e.g., time series or natural language processing tasks).
- 23. Implementation of autoencoder •Autoencoders are powerful neural networks with diverse applications in unsupervised learning, including dimensionality reduction, feature extraction, and data compression • To implement an autoencoder : • 1- you would typically define two separate modules for the encoder and decoder, and then combine them in a higher-level module. • 2- You then train the autoencoder using backpropagation and gradient descent, minimizing the reconstruction error.
- 24. What Are theApplications of Autoencoders? • 1-Dimensionality Reduction • Autoencoders can reduce the dimensionality of input data by learning a compact and efficient representation in the latent space. This can be helpful for visualization, data compression, and speeding up other machine learning algorithms. • 2-Feature Learning • Autoencoders can learn meaningful features from input data, which can be used for downstream machine-learning tasks like classification, clustering, or regression. • 3-Anomaly Detection • By training an autoencoder on normal data instances, it can learn to reconstruct those instances with low error. When presented with an anomalous data point, the autoencoder will likely have a higher reconstruction error, which can be used to identify outliers or anomalies.
- 25. Anomaly Detection • Autoencodersare used largely for anomaly detection: As we know, autoencoders create encodings that basically captures the relationship among data. • Now, if we train our autoencoder on a particular dataset, the encoder and decoder parameters will be trained to represent the relationships on the datasets the best way. • Thus, we will be able to recreate any data given from that kind of dataset in the best way. • So, if data from that particular dataset is sent through the autoencoder, the reconstruction error is less. • But if some other kind of data is sent through the autoencoder it will generate a huge reconstruction error. If we can apply a correct cutoff we will be able to create an anomaly detector.
- 26. Cont. • 4-Denoising Images •Autoencoders can be trained to reconstruct clean input data from noisy versions. The denoising autoencoder learns to remove the noise and produce a clean version of the input data. • 5-Image Inpainting • As shown in Figure 10, autoencoders can fill in missing or corrupted parts of an image by learning the underlying structure and patterns in the data.
- 27. Denoising Images • Autoencodersare used for Noise Removal: If we can pass the noisy data as input and clean data as output and train an autoencoder on such given data pairs, trained Autoencoders can be highly useful for noise removal. • This is because noise points usually do not have any correlations. Now, as the autoencoders need to represent the data in the lowest dimensions, the encodings usually have only the important relations there exists, rejecting the random ones. • So, the decoded data coming out as output of an autoencoder is free of all the extra relations and hence the noise.
- 28. Cont. • 6-Generative Modeling •Variational autoencoders (VAEs) and other generative variants can generate new, realistic data samples by learning the data distribution during training. • This can be useful for data augmentation or creative applications. • Before the GAN’s came into existence, Autoencoders were used as generative models. One of the modified approaches of autoencoders, variational autoencoders are used for generative purposes. • 7-Recommender Systems • Autoencoders can be used to learn latent representations of users and items in a recommender system, which can then predict user preferences and make personalized recommendations. • 8-Sequence-to-Sequence Learning • Autoencoders can be used for sequence-to-sequence tasks, such as machine translation or text summarization, by adapting their architecture to handle sequential input and output data.
- 29. Cont. • 9-Image Segmentation •Autoencoders are commonly utilized in semantic segmentation as well. • A notable example is SegNet (Figure 11), a model designed for pixel-wise, multi-class segmentation on urban road scene datasets. • This model was created by researchers from the University of Cambridge’s Computer Vision Group.
- 30. How Are Autoencode rs Different fromGANs? For those familiar with Generative Adversarial Networks (GANs), you might wonder how Autoencoders, which also learn to generate images, differ from GANs . While both Autoencoders and GANs are generative models, they differ in : 1 - Architecture 2 - Training Process 3 - Objectives
- 31. Architectu re Autoencoders consist ofan encoder and a decoder. The encoder compresses the input data into a lower-dimensional latent representation while the decoder reconstructs the input data from the latent representation . GANs consist of a generator and a discriminator. The generator creates fake samples from random noise, and the discriminator tries to differentiate between real samples from the dataset and fake samples produced by the generator .
- 32. Training Process • Autoencoders aretrained using a reconstruction error, which measures the difference between the input and reconstructed data. The training process aims to minimize this error. • GANs use a two-player minimax game during training. The generator tries to produce samples that the discriminator cannot distinguish from real samples, while the discriminator tries to improve its ability to differentiate between real and fake samples. The training process involves finding an equilibrium between the two competing networks.
- 33. Objectives • Autoencoders areprimarily used for dimensionality reduction, data compression, denoising, and representation learning. • GANs are primarily used for generating new, realistic samples, often in the domain of images, but also in other data types such as text, audio, and more.
- 34. Strength and weakness Whileboth autoencoders and GANs can generate new samples, they have different strengths and weaknesses . Autoencoders generally produce smoother and more continuous outputs , while GANs can generate more realistic and diverse examples but may suffer from issues like mode collapse . The choice between the two models depends on the specific problem and desired outcomes .
- 35.
- 36. References • https://machinelearningmastery.com/autoencoder-for-classification/ • https://www.datacamp.com/tutorial/introduction-to-autoencoders •https://towardsdatascience.com/understanding-autoencoders-with-an-example-a-step-by-step-tutorial-693c3a4e9836 • https://www.edureka.co/blog/autoencoders-tutorial/ • https://pyimagesearch.com/2020/02/17/autoencoders-with-keras-tensorflow-and-deep-learning/ • https://www.geeksforgeeks.org/auto-encoders/ • https://www.analyticsvidhya.com/blog/2021/07/image-denoising-using-autoencoders-a-beginners-guide-to-deep- learning-project/ • https://compneuro.neuromatch.io/tutorials/Bonus_Autoencoders/student/Bonus_Tutorial3.html • https://jaan.io/what-is-variational-autoencoder-vae-tutorial/ • https://lazyprogrammer.me/a-tutorial-on-autoencoders/ • https://medium.com/data-science-365/an-introduction-to-autoencoders-in-deep-learning-ab5a5861f81e • https://www.simplilearn.com/tutorials/deep-learning-tutorial/what-are-autoencoders-in-deep-learning • https://www.slideshare.net/harishr2301/autoencoder-141008858 • https://arxiv.org/abs/2201.03898 • https://www.analyticsvidhya.com/blog/2021/10/an-introduction-to-autoencoders-for-beginners/ • https://www.jeremyjordan.me/autoencoders/ • https://pyimagesearch.com/2023/07/10/introduction-to-autoencoders/ • https://towardsdatascience.com/introduction-to-autoencoders-7a47cf4ef14b
Editor's Notes
- #5 https://pyimagesearch.com/2023/07/10/introduction-to-autoencoders/
- #6 https://pyimagesearch.com/2023/07/10/introduction-to-autoencoders/
- #7 https://towardsdatascience.com/introduction-to-autoencoders-7a47cf4ef14b
- #8 https://towardsdatascience.com/introduction-to-autoencoders-7a47cf4ef14b
- #9 https://towardsdatascience.com/introduction-to-autoencoders-7a47cf4ef14b
- #10 https://pyimagesearch.com/2023/07/10/introduction-to-autoencoders/
- #11 https://pyimagesearch.com/2023/07/10/introduction-to-autoencoders/
- #12 https://pyimagesearch.com/2023/07/10/introduction-to-autoencoders/
- #13 https://pyimagesearch.com/2023/07/10/introduction-to-autoencoders/
- #14 https://towardsdatascience.com/introduction-to-autoencoders-7a47cf4ef14b
- #15 https://towardsdatascience.com/introduction-to-autoencoders-7a47cf4ef14b
- #16 https://www.jeremyjordan.me/autoencoders/
- #17 https://pyimagesearch.com/2023/07/10/introduction-to-autoencoders/
- #18 https://pyimagesearch.com/2023/07/10/introduction-to-autoencoders/
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