Mdthbs

I want to change the color of a section of a document.
The command to do so is

{color{red} section{Introduction}}

(Merely doing section{color{Introduction}} does not change the color of the section number).

So when I carry out the command above, it slightly adjusts the vertical space of all of the text in the entire document. This can be observed in the gif https://imgur.com/a/RLpPLU6

So, how do I carry out color changes without changing the vertical spacing, etc ?

Edit : A minimal code snippet and the effect of changing color can be seen here https://imgur.com/a/pI3NmuX (no other change except color)

documentclass{article} % For LaTeX2e

usepackage{xcolor}



title{Generative Adversarial Nets}



begin{document}



section{Introduction}



The promise of deep learning is to discover rich, hierarchical models that represent

probability distributions over the kinds of data encountered in artificial intelligence applications,

 such as natural images, audio waveforms containing speech, and symbols in natural language corpora.



{color{red} section{Next Section}}



This framework can yield specific training algorithms for many kinds of model and optimization

algorithm. In this article, we explore the special case when the generative model generates

samples by passing random noise through a multilayer perceptron, and the discriminative model

is also a multilayer perceptron. We refer to this special case as {em adversarial nets}.

In this case, we can train both models using only

the highly successful backpropagation and dropout algorithms

and sample from the generative

model using only forward propagation. No approximate inference or Markov chains are necessary.





end{document}

I previously thought the culprit was the nips14submit_e (which can be found here (https://pastebin.com/aw96SzXB)), but it doesn't seem to be

I want to change the color of a section of a document ... one section at a time.

If you prefer using high-level commands and letting LaTeX perform most of the hard work for you "behind the scenes", while not having to be concerned with subtle positional shifts, I recommend loading the sectsty package and using its sectionfont command. As the following example shows, one can employ sectionfont{color{...}} multiple times.

documentclass{article}

usepackage{sectsty} % for "sectionfont" macro

usepackage[dvipsnames]{xcolor}



begin{document}



sectionfont{color{RubineRed}}

section{Introduction}



sectionfont{color{Dandelion}}

section{Literature}



sectionfont{color{Aquamarine}}

section{Data}



sectionfont{color{PineGreen}}

section{Methods}



sectionfont{color{NavyBlue}}

section{Results}



sectionfont{color{Violet}}

section{Conclusion}



end{document}

As you can check with showoutput there are no spacing differences in the supplied example in the question with and without color{red} so it isn't an example of this problem...

However the issue can occur (and is shown in the first posted gif with the centred abstract).

A real example is

documentclass{article} % For LaTeX2e

usepackage{xcolor}

showoutput

showboxdepth3

title{Generative Adversarial Nets}



begin{document}



begin{center}

  zzzzz

end{center}



{%color{red}

section{Next Section}}



This framework can yield specific training algorithms for many kinds of model and optimization

algorithm. In this article, we explore the special case when the generative model generates

samples by passing random noise through a multilayer perceptron, and the discriminative model

is also a multilayer perceptron. We refer to this special case as {em adversarial nets}.

In this case, we can train both models using only

the highly successful backpropagation and dropout algorithms

and sample from the generative

model using only forward propagation. No approximate inference or Markov chains are necessary.





end{document}

If you uncomment the colour command you get additional space before the heading as the colour whatsit node prevents the section command "seeing" the vertical space already added by the end{center} and so it adds its full specified amount rather than merging the spaces specified by end{center} and section.

The trick is to do colour changes as far as possible in horizontal not vertical mode, so for example this works

documentclass{article} % For LaTeX2e

usepackage{xcolor}

showoutput

showboxdepth3

title{Generative Adversarial Nets}



begin{document}

color{red}



begin{center}

  textcolor{black}{zzzzz}

end{center}





section{Next Section}



mbox{}color{black}This framework can yield specific training algorithms for many kinds of model and optimization

algorithm. In this article, we explore the special case when the generative model generates

samples by passing random noise through a multilayer perceptron, and the discriminative model

is also a multilayer perceptron. We refer to this special case as {em adversarial nets}.

In this case, we can train both models using only

the highly successful backpropagation and dropout algorithms

and sample from the generative

model using only forward propagation. No approximate inference or Markov chains are necessary.





end{document}

Or perhaps nicer the following which just locally tacks the color on to the Large used by the section heading

documentclass{article} % For LaTeX2e

usepackage{xcolor}

showoutput

showboxdepth3

letrealLargeLarge

title{Generative Adversarial Nets}



begin{document}





begin{center}

  zzzzz

end{center}



%defLarge{realLargecolor{red}}

section{Next Section}

%letLargerealLarge



This framework can yield specific training algorithms for many kinds of model and optimization

algorithm. In this article, we explore the special case when the generative model generates

samples by passing random noise through a multilayer perceptron, and the discriminative model

is also a multilayer perceptron. We refer to this special case as {em adversarial nets}.

In this case, we can train both models using only

the highly successful backpropagation and dropout algorithms

and sample from the generative

model using only forward propagation. No approximate inference or Markov chains are necessary.





end{document}