Results 51 to 60 of about 119,739 (184)

Improving Sampling from Generative Autoencoders with Markov Chains [PDF]

, 2016
We focus on generative autoencoders, such as variational or adversarial autoencoders, which jointly learn a generative model alongside an inference model.
Arulkumaran, K, Bharath, AA, Creswell, A
core  

Hashing with binary autoencoders

, 2015
An attractive approach for fast search in image databases is binary hashing, where each high-dimensional, real-valued image is mapped onto a low-dimensional, binary vector and the search is done in this binary space.
Carreira-Perpiñán, Miguel Á., Raziperchikolaei, Ramin   +1 more
core   +1 more source

A Manifold Learning Perspective on Representation Learning: Learning Decoder and Representations without an Encoder

Entropy, 2021
Autoencoders are commonly used in representation learning. They consist of an encoder and a decoder, which provide a straightforward method to map n-dimensional data in input space to a lower m-dimensional representation space and back.
Viktoria Schuster, Anders Krogh
doaj   +1 more source

Subitizing with Variational Autoencoders

, 2018
Numerosity, the number of objects in a set, is a basic property of a given visual scene. Many animals develop the perceptual ability to subitize: the near-instantaneous identification of the numerosity in small sets of visual items.
A Nieder, A Nieder, BM Harvey, BR Jansen, C Arteta, D Burr, E Walach, EL Kaufman, G Lakoff, G Lemaître, H Davis, I Goodfellow, I Stoianov, J Zhang, L Feigenson, M Piazza, M Poncet, M-M Cheng, MC Franka, Olga Russakovsky, P Viswanathan, S Dehaene, T-Y Lin, WS Jevons, Y Hu, Y LeCun   +25 more
core   +1 more source

Fast and Effective Techniques for LWIR Radiative Transfer Modeling: A Dimension-Reduction Approach

Remote Sensing, 2019
The increasing spatial and spectral resolution of hyperspectral imagers yields detailed spectroscopy measurements from both space-based and airborne platforms.
Nicholas Westing, Brett Borghetti, Kevin C. Gross   +2 more
doaj   +1 more source

Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems

, 2019
We show that end-to-end learning of communication systems through deep neural network (DNN) autoencoders can be extremely vulnerable to physical adversarial attacks.
Larsson, Erik G., Sadeghi, Meysam
core   +1 more source

Biometric-Based Key Generation and User Authentication Using Acoustic Characteristics of the Outer Ear and a Network of Correlation Neurons

Sensors, 2022
Trustworthy AI applications such as biometric authentication must be implemented in a secure manner so that a malefactor is not able to take advantage of the knowledge and use it to make decisions.
Alexey Sulavko
doaj   +1 more source

Enhancing anomaly detection with topology-aware autoencoders

Machine Learning: Science and Technology
Anomaly detection in high-energy physics is essential for identifying new physics beyond the Standard Model. Autoencoders provide a signal-agnostic approach but are limited by the topology of their latent space.
Vishal S Ngairangbam, Błażej Rozwoda, Kazuki Sakurai, Michael Spannowsky   +3 more
doaj   +1 more source

Interpretability-Aware Industrial Anomaly Detection Using Autoencoders

IEEE Access, 2023
The past decade has witnessed wide applications of deep neural networks in anomaly detection. However, the dearth of interpretability in neural networks often hinders their reliability, especially for industrial applications where practical users heavily
Rui Jiang, Yijia Xue, Dongmian Zou
doaj   +1 more source

Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders [PDF]

Computer Vision and Pattern Recognition, 2018
Many approaches in generalized zero-shot learning rely on cross-modal mapping between the image feature space and the class embedding space. As labeled images are expensive, one direction is to augment the dataset by generating either images or image ...
Edgar Schönfeld, Sayna Ebrahimi, Samarth Sinha, Trevor Darrell, Zeynep Akata   +4 more
semanticscholar   +1 more source