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The method indicates to be better than conventional autoencoder with more hidden layers.
Book ChapterDOI
B. Chandra, Rajesh K. Sharma 
03 Nov 2014
26 Citations
This enables the denoising autoencoder to learn the input manifold in greater details.
Comparisons with other results from the literature reveal that the proposed wavelet coder is quite competitive.<<ETX>>
Comparisons with other results from the literature reveal that the proposed wavelet coder is quite competitive.

Related Questions

How effective are deep learning-based autoencoder models in removing noise from images compared to traditional methods?4 answersDeep learning-based autoencoder models have shown to be effective in removing noise from images compared to traditional methods. These models, such as the proposed Convolutional Denoising Autoencoderand, utilize deep convolutional neural networks to generate noisy images and extract clean latent images by removing the noise. This approach has been found to outperform current denoising techniques, including traditional filters like wiener, median, and mean filters. The use of deep learning-based autoencoders allows for better denoising results, as evidenced by factors such as PSNR, MSE, training loss, and training accuracy. Additionally, these models can effectively suppress multiple noises with various noise levels using a single denoising model. Overall, deep learning-based autoencoder models offer a more accurate and efficient solution for noise removal in image processing compared to traditional methods.
What are the types of deep Autoencoders?5 answersDeep autoencoders are a type of artificial neural network used for unsupervised learning. They are capable of learning efficient data coding without the need for labeled data. Different types of deep autoencoders mentioned in the abstracts include denoising autoencoders, sparse autoencoders, and overcomplete autoencoders. These different types of autoencoders have specific architectures and training strategies that make them suitable for different tasks. Denoising autoencoders are designed to handle noisy input data, while sparse autoencoders impose sparsity constraints on the learned representations. Overcomplete autoencoders have more hidden units than input units, allowing them to learn more complex features. Each type of autoencoder has its own advantages and is used in various applications in fields such as transfer learning, acoustic anomaly detection, and seismological applications.
How can deep learning models be used to learn wavelet packet transforms?4 answersDeep learning models can be used to learn wavelet packet transforms by incorporating the wavelet packet transform (WPT) as a preprocessing step before applying the deep learning model. One approach is to use the WPT to extract time-frequency domain information from the data, which is then fed into a convolutional neural network (CNN) for feature extraction and classification. Another approach is to use the maximum overlap discrete wavelet transform (MODWT) to decompose the input variables and explore the impact of wavelet transform in improving the simulations. Additionally, the fast wavelet transform can be applied to compress linear layers in neural networks, allowing for efficient representation of the linear layers with significantly fewer parameters.
How can autoencoders be used to compress images?3 answersAutoencoders can be used to compress images by using neural network models. One approach is to use a two-layered autoencoder model for video coding, where the multi-layer encoder provides scalability and allows for decoupling the traditional video coding implementation from the neural network solutions. This approach enables the base layer bitstream to be decoded without running the decoding process with the neural network, providing better compression rates and quality results. Another approach is to use a three-layer autoencoder model for compressing and denoising grayscale medical images. This model adds Gaussian noise to the images, passes them through convolution and max pool layers, and then reverses the process for image denoising. The trained model achieves high peak signal-to-noise ratio (PSNR) and compression ratios, making it effective for medical image analysis.
What are the advantages of using autoencoders for image encryption?5 answersAutoencoders offer several advantages for image encryption. Firstly, they can compress the image in a lossless manner, ensuring that no information is lost during the encryption process. Secondly, autoencoders remove the spatial information from the encrypted representation, making it more secure and difficult to decipher. Additionally, using deep learning-based architectures like autoencoders can provide high-performance scores and fruitful results in cryptography. Autoencoders also allow for the control of data noises, improving the encryption performance. Furthermore, the use of autoencoders in image encryption can enhance imperceptibility, as demonstrated by high peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) values. Overall, autoencoders offer a powerful and effective approach for image encryption, combining compression, security, and imperceptibility.
What are DSP wavelets?17 answers

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