Task Understanding from Confushing Multitask Data: Difference between revisions
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'''Kaggle Fashion Product''': This data set has more observations than the "colored-MNIST" data and consists of pictures labelled with either the “Gender”, “Category”, and “Color” of the clothing item. | '''Kaggle Fashion Product''': This data set has more observations than the "colored-MNIST" data and consists of pictures labelled with either the “Gender”, “Category”, and “Color” of the clothing item. | ||
==Use of | ==Use of Pre-Trained CNN Feature Layers== | ||
In the Kaggle Fashion Product experiment, each of the 3 classification algorithms <math>f_j</math> consist of fully-connected layers that have been attatched to feature-identifying layers from pre-trained Convolutional Neural Networks. | In the Kaggle Fashion Product experiment, each of the 3 classification algorithms <math>f_j</math> consist of fully-connected layers that have been attatched to feature-identifying layers from pre-trained Convolutional Neural Networks. | ||
==Metrics of Confusing Supervised Learning== | ==Metrics of Confusing Supervised Learning== | ||
==Results== | ==Results== | ||
Revision as of 17:00, 15 November 2020
Task Understanding from Confusing Multi-task Data
Presented By aslkdfj;awekrf
1. Introduction
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Hello
[math]\displaystyle{ \begin{align*} e & = \pi = \sqrt{g} \end{align*} }[/math]
2. Related Work
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[math]\displaystyle{ \begin{align*} e & = \text{Hellow}\\ & = \dfrac{123}{4}\\ \end{align*} }[/math]
[math]\displaystyle{
\begin{align*}
h+1 & = \dfrac{abc}{\text{def}}\\
& = \dfrac{123}{4}\\
\end{align*}
}[/math]
Experiment
Setup
3 data sets are used to compare CSL to existing methods, 1 function regression task and 2 image classification tasks.
Function Regression: The function regression data comes in the form of [math]\displaystyle{ (x_i,y_i),i=1,...,m }[/math] pairs. However, unlike typical regression problems, there are multiple [math]\displaystyle{ f_j(x),j=1,...,n }[/math] mapping functions, so the goal is to recover both the mapping functions [math]\displaystyle{ f_j }[/math] as well as determine which mapping function corresponds to each of the [math]\displaystyle{ m }[/math] observations.
Colorful-MNIST: The first image classification data set consists of the MNIST digit data that has been colored. Each observation in this modified set consists of a colored image ([math]\displaystyle{ x_i }[/math]) and either the color, or the digit it represents ([math]\displaystyle{ y_i }[/math]). The goal is to recover the classification task ("color" or "digit") for each observation and construct the 2 classifiers for both tasks.
Kaggle Fashion Product: This data set has more observations than the "colored-MNIST" data and consists of pictures labelled with either the “Gender”, “Category”, and “Color” of the clothing item.
Use of Pre-Trained CNN Feature Layers
In the Kaggle Fashion Product experiment, each of the 3 classification algorithms [math]\displaystyle{ f_j }[/math] consist of fully-connected layers that have been attatched to feature-identifying layers from pre-trained Convolutional Neural Networks.