High-dimensional features are extensively accessible in machine learning and computer vision areas. How to learn an efficient feature representation for specific learning tasks is invariably a crucial ...

In electroencephalography (EEG) classification paradigms, data from a target subject is often difficult to obtain, leading to difficulties in training a robust deep learning network. Transfer learning ...

Tools for training and using unsupervised autoencoders and supervised deep learning classifiers for hyperspectral data. Documentation available here. Source code available on Github. Autoencoders are ...

Unsupervised deep learning approach for anti-spoofing in contactless fingerprint recognition. Trains on genuine samples only using autoencoder with convolutional attention. Detects various ...

In this study, we present a sophisticated hybrid machine-learning framework that significantly improves the accuracy of predicting hydrogen storage capacities in metal hydrides. This is a critical ...

At a presentation during Google I/O 2019, Google announced TensorFlow Graphics, a library for building deep neural networks for unsupervised learning tasks in computer vision. The library contains 3D ...

We introduce unFEAR, Unsupervised Feature Extraction Clustering, to identify economic crisis regimes. Given labeled crisis and non-crisis episodes and the corresponding features values, unFEAR uses ...

Convolutional Autoencoder. Convolutional Autoencoder is a variant of Convolutional Neural Networks that are used as the tools for unsupervised learning of convolution filters. They are generally ...