Task Understanding from Confushing Multitask Data

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.

Usa 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.