Bird Swarm Optimization-based Stacked Autoencoder Deep Learning for Umpire Detection and Classification

Stacked Robust Autoencoder for Classification

In this work we propose an lp-norm data fidelity constraint for training the autoencoder. Usually the Euclidean distance is used for this purpose; we generalize the l2-norm to the lp-norm; smaller values of p make the problem robust to outliers. The ensuing optimization problem is solved using the Augmented Lagrangian approach. The proposed lp -norm Autoencoder has been tested on benchmark deep...

[Article] EEG Based Eye State Classification using Deep Belief Network and Stacked AutoEncoder

Remote Sensing Image Classification Based on Stacked Denoising Autoencoder

Focused on the issue that conventional remote sensing image classification methods have run into the bottlenecks in accuracy, a new remote sensing image classification method inspired by deep learning is proposed, which is based on Stacked Denoising Autoencoder. First, the deep network model is built through the stacked layers of Denoising Autoencoder. Then, with noised input, the unsupervised ...

Deep learning for detection of bird vocalisations

This work focuses on reliable detection of bird sound emissions as recorded in the open field. Acoustic detection of avian sounds can be used for the automatized monitoring of multiple bird taxa and querying in long-term recordings for species of interest for researchers, conservation practitioners, and decision makers. Recordings in the wild can be very noisy due to the exposure of the microph...

Deep Dictionary Learning vs Deep Belief Network vs Stacked Autoencoder: An Empirical Analysis

A recent work introduced the concept of deep dictionary learning. The first level is a dictionary learning stage where the inputs are the training data and the outputs are the dictionary and learned coefficients. In subsequent levels of deep dictionary learning, the learned coefficients from the previous level acts as inputs. This is an unsupervised representation learning technique. In this wo...