scholarly journals Infinite Variational Autoencoder for Semi-Supervised Learning

2017 ◽  
Author(s):  
M. Ehsan Abbasnejad ◽  
Anthony Dick ◽  
Anton van den Hengel
Keyword(s):  
Supervised Learning ◽  
Variational Autoencoder

A Semi-Supervised Learning Method for MiRNA-Disease Association Prediction Based on Variational Autoencoder

2021 ◽  
pp. 1-1
Author(s):  
Cunmei Ji ◽  
Yu-Tian Wang ◽  
Zhen Gao ◽  
Lei Li ◽  
Jian-Cheng Ni ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Disease Association ◽  
Learning Method ◽  
Variational Autoencoder

scholarly journals Non-I.I.D. Multi-Instance Learning for Predicting Instance and Bag Labels with Variational Auto-Encoder

2021 ◽  
Author(s):  
Weijia Zhang
Keyword(s):  
Medical Imaging ◽  
Supervised Learning ◽  
Real World ◽  
State Of The Art ◽  
Experimental Results ◽  
Weakly Supervised Learning ◽  
Variational Autoencoder ◽  
Label Prediction ◽  
Weakly Supervised ◽  
Better Than

Multi-instance learning is a type of weakly supervised learning. It deals with tasks where the data is a set of bags and each bag is a set of instances. Only the bag labels are observed whereas the labels for the instances are unknown. An important advantage of multi-instance learning is that by representing objects as a bag of instances, it is able to preserve the inherent dependencies among parts of the objects. Unfortunately, most existing algorithms assume all instances to be identically and independently distributed, which violates real-world scenarios since the instances within a bag are rarely independent. In this work, we propose the Multi-Instance Variational Autoencoder (MIVAE) algorithm which explicitly models the dependencies among the instances for predicting both bag labels and instance labels. Experimental results on several multi-instance benchmarks and end-to-end medical imaging datasets demonstrate that MIVAE performs better than state-of-the-art algorithms for both instance label and bag label prediction tasks.

scholarly journals Interpretable Feature Generation in ECG Using a Variational Autoencoder

2021 ◽  
Vol 12 ◽  
Author(s):  
V. V. Kuznetsov ◽  
V. A. Moskalenko ◽  
D. V. Gribanov ◽  
Nikolai Yu. Zolotykh
Keyword(s):  
Cardiovascular Diseases ◽  
Supervised Learning ◽  
Cardiac Cycle ◽  
Ecg Signal ◽  
Feature Generation ◽  
Maximum Mean Discrepancy ◽  
Variational Autoencoder ◽  
Electrocardiogram Ecg

We propose a method for generating an electrocardiogram (ECG) signal for one cardiac cycle using a variational autoencoder. Our goal was to encode the original ECG signal using as few features as possible. Using this method we extracted a vector of new 25 features, which in many cases can be interpreted. The generated ECG has quite natural appearance. The low value of the Maximum Mean Discrepancy metric, 3.83 × 10−3, indicates good quality of ECG generation too. The extracted new features will help to improve the quality of automatic diagnostics of cardiovascular diseases. Generating new synthetic ECGs will allow us to solve the issue of the lack of labeled ECG for using them in supervised learning.

scholarly journals Generating Video From Images using GAN and CVAE

2020 ◽  
Vol 8 (5) ◽  
pp. 1401-1404
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Generative Models ◽  
Generative Model ◽  
Learning Systems ◽  
Training Data ◽  
Variational Autoencoder ◽  
Discriminative Model ◽  
To Come

In a given scene, people can often easily predict a lot of quick future occasions that may occur. However generalized pixel-level expectation in Machine Learning systems is difficult in light of the fact that it struggles with the ambiguity inherent in predicting what's to come. However, the objective of the paper is to concentrate on predicting the dense direction of pixels in a scene — what will move in the scene, where it will travel, and how it will deform through the span of one second for which we propose a conditional variational autoencoder as a solution for this issue. We likewise propose another structure for assessing generative models through an adversarial procedure, wherein we simultaneously train two models, a generative model G that catches the information appropriation, and a discriminative model D that gauges the likelihood that an example originated from the training data instead of G. We focus on two uses of GANs semi-supervised learning, and the age of pictures that human's find visually realistic. We present the Moments in Time Dataset, an enormous scale human-clarified assortment of one million short recordings relating to dynamic situations unfolding within three seconds.

scholarly journals Toward Automatically Labeling Situations in Soccer

2021 ◽  
Vol 3 ◽  
Author(s):  
Dennis Fassmeyer ◽  
Gabriel Anzer ◽  
Pascal Bauer ◽  
Ulf Brefeld
Keyword(s):  
Supervised Learning ◽  
Feature Space ◽  
Feature Representation ◽  
Unlabeled Data ◽  
Training Data ◽  
Automatic Annotation ◽  
Learning Problem ◽  
Large Margin ◽  
Variational Autoencoder

We study the automatic annotation of situations in soccer games. At first sight, this translates nicely into a standard supervised learning problem. However, in a fully supervised setting, predictive accuracies are supposed to correlate positively with the amount of labeled situations: more labeled training data simply promise better performance. Unfortunately, non-trivially annotated situations in soccer games are scarce, expensive and almost always require human experts; a fully supervised approach appears infeasible. Hence, we split the problem into two parts and learn (i) a meaningful feature representation using variational autoencoders on unlabeled data at large scales and (ii) a large-margin classifier acting in this feature space but utilize only a few (manually) annotated examples of the situation of interest. We propose four different architectures of the variational autoencoder and empirically study the detection of corner kicks, crosses and counterattacks. We observe high predictive accuracies above 90% AUC irrespectively of the task.

A Supervised Learning Approach for Dynamic Sampling (SLADS) in Raman Hyperspectral Imaging

2018 ◽  
Vol 2018 (15) ◽  
pp. 132-1-1323
Author(s):  
Shijie Zhang ◽  
Zhengtian Song ◽  
G. M. Dilshan P. Godaliyadda ◽  
Dong Hye Ye ◽  
Atanu Sengupta ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Hyperspectral Imaging ◽  
Learning Approach ◽  
Dynamic Sampling
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