A Converting Autoencoder Toward Low-latency and Energy-efficient DNN ...
Mar 11, 2024 · Reducing inference time and energy usage while maintaining prediction accuracy has become a significant concern for deep neural networks (DNN) inference on resource …
The activation function of the hidden layer is linear and hence the name linear autoencoder. The above network uses the linear activation function and works for the case that the data lie on a …
All-optical autoencoder machine learning framework using linear ...
Mar 21, 2025 · Here, we propose an all-optical autoencoder (OAE) framework that linearly encodes the input wavefield into a prior shape distribution in the diffractive latent space (DLS) …
Deep autoencoders for feature learning with embeddings for ...
Jun 6, 2021 · This method learns linear and nonlinear latent variables from both the autoencoder features as well as the embeddings, resulting in a rich feature set that is exploited by the DNN …
EncodeNet: A Framework for Boosting DNN Accuracy with
Dec 2, 2024 · In this paper, we generalize the design of our Converting Autoencoder by systematically deriving its structure from given baseline DNN, making it applicable to a large …
Linearly Recurrent Autoencoder Networks for Learning Dynamics
A new neural network architecture combining an autoencoder with linear recurrent dynamics in the encoded state is used to learn a low-dimensional and highly informative Koopman …
A Converting Autoencoder Toward Low-latency and Energy-efficient DNN ...
Mar 11, 2024 · We present CBNet, a low-latency and energy-efficient DNN inference framework tailored for edge devices. It utilizes a “converting” autoencoder to efficiently transform hard …
Deep Autoencoder Neural Networks: A Comprehensive Review …
Mar 15, 2025 · Autoencoders have become a fundamental technique in deep learning (DL), significantly enhancing representation learning across various domains, including image …
Autoencoders in Machine Learning - GeeksforGeeks
Mar 1, 2025 · Autoencoders aim to minimize reconstruction error which is the difference between the input and the reconstructed output. They use loss functions such as Mean Squared Error …
Linear and convolutional autoencoders | Documentation
In this tutorial, our goal is to compare the performance of two types of autoencoders, a linear autoencoder and a convolutional autoencoder, on reconstructing the Fashion-MNIST images.