Dec 6, 2020 · The encoder can then be used as a data preparation technique to perform feature extraction on raw data that can be used to train a different machine learning model. In this …

Oct 9, 2023 · Autoencoders are a class of artificial neural networks used in tasks like data compression and reconstruction. In this article, we will delve into the world of autoencoders, …

Nov 30, 2015 · Training a deep network for feature extraction and classification includes unsupervised pretraining and supervised fine-tuning. We utilized stacked denoise autoencoder …

Oct 7, 2024 · Autoencoders are a powerful tool used in machine learning for feature extraction, data compression, and image reconstruction. These neural networks have made significant …

Autoencoders are a type of neural network which generates an “n-layer” coding of the given input and attempts to reconstruct the input using the code generated. The Autoencoder architecture …

In this work, we propose a novel discriminative autoencoder. Use of supervised discriminative learning ensures that the learned representation is robust to variations commonly encountered …

We provide an example of how automatically extracted features can be used in a classification problem for the detection of student affect. In this example, student boredom was detected at …

Aug 30, 2023 · To evaluate the efficiency, we employ simple autoencoder and U-Net models that take both data features and their relationships into consideration.

Oct 4, 2024 · Best way to use the Encoder part with pytorch code implementation for feature extraction. Why Use an Autoencoder and Focus on the Encoder? 1. Unsupervised Pretraining …

May 19, 2025 · Recent methods like IamCSC 6 and \({\text{HI}}^{2}\, {\text{D}}^{2}\) FNet 7 enhance the HSI-LiDAR fusion, improving feature extraction and classification performance for …