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add some dataset infos

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lukas-heiligenbrunner 2023-06-03 23:20:07 +02:00
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@ -98,6 +98,12 @@ Labeling datasets is commonly seen as an expensive task and wants to be avoided
Thats why there is a machine-learning field called Semi-Supervised learning. Thats why there is a machine-learning field called Semi-Supervised learning.
The general approach is to train a model that predicts Pseudo-Labels which then can be used to train the main model. The general approach is to train a model that predicts Pseudo-Labels which then can be used to train the main model.
The goal of this paper is a video action recognition.
Given are approximately 10 seconds long videos which should be classified.
In this paper datasets with 400 and 101 different classes are used.
The papers approach is tested with 1\% and 10\% of known labels of all data points.
The used model depends on the exact usecase but in this case a 3D-ResNet50 and 3D-ResNet18 are used.
\section{Semi-Supervised learning}\label{sec:semi-supervised-learning} \section{Semi-Supervised learning}\label{sec:semi-supervised-learning}
In traditional supervised learning we have a labeled dataset. In traditional supervised learning we have a labeled dataset.
Each datapoint is associated with a corresponding target label. Each datapoint is associated with a corresponding target label.
@ -208,7 +214,7 @@ And as a backbone model a 3D-ResNet18 and 3D-ResNet50 are used.
\end{figure} \end{figure}
\section{Further schemes}\label{sec:further-schemes} \section{Further schemes}\label{sec:further-schemes}
How the pseudo-labels are generated my impact the overall performance. How the pseudo-labels are generated may impact the overall performance.
In this paper the pseudo-labels are obtained by the cross-model approach. In this paper the pseudo-labels are obtained by the cross-model approach.
But there might be other strategies. But there might be other strategies.
For example: For example: