A Convolutional Autoencoder (CAE) is a type of autoencoder specifically designed for image data, using convolutional neural networks (CNNs) for both the encoder and decoder. The encoder extracts features from the input image using convolutional layers, while the decoder reconstructs the output image using deconvolution layers. The process involves flattening the final convolutional layer output, mapping it to a lower-dimensional latent space, and reshaping it for the decoder to reconstruct the image.
Different types of loss functions, such as Mean Squared Error (MSE) and Binary Cross-Entropy (BCE), are used to measure the reconstruction error and train the autoencoder. MSE calculates the squared difference between input and output pixels, while BCE is used for binary or normalized data. Regularization techniques like sparsity can be added to the loss function to focus on meaningful features and filter out noise.
A Python implementation of a basic autoencoder using the Keras library is provided, showcasing model architecture, training, evaluation, and visualization of encoded representations and reconstructed images. The importance of regularization techniques like sparsity in ensuring meaningful feature extraction is highlighted, with a closing note on exploring KL-divergence in the next part of the series.
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Source link: https://medium.com/@ompramod9921/autoencoders-explained-1fa7f4c32f12?source=rss——ai-5