try.tex

\documentclass{article}
\title{Test title}
\author{ zjj }
\date{\today}
\usepackage[ruled]{algorithm2e}
\usepackage{amsmath}
\usepackage{amssymb}
\begin{document}
\begin{algorithm} 
	\caption{ weakly supervised learning for point cloud semantic segmentation} 
	\KwIn{Point cloud $ P \in \mathbb{R}^{N \times D} $, Labels $y \in \mathbb{Z}^M (M \ll N)$} 
	\KwOut{Predictions $ p \in \mathbb{Z}^{N \times C} $} 
	// Stage 1\;
	\For{$epoch \gets  1 \ldots 100$} 
	{ 
		\For{each mini-batch $B$}
		{
			Train one step:\\
			$w = w - \eta \nabla (l_{seg}(y,p_{l}|w)+
			\lambda_{ent}l_{ent}(p_{u}|w)+
			\lambda_{cc}l_{cc}(p^{teacher},p|w))$\;
			\# $w$ is learned parameters of the student model, $p_{l}$ and $p_{u}$ are predictions in labeld and unlabeled data\;
			Updata Teacher model:\\
			$w^{teacher}_{t} = \alpha \cdot w^{teacher}_{t-1} + (1-\alpha)w^{student}_{t}$\;
		}
	} 
	// Stage 2\;
	\For{$epoch \gets  1 \ldots 100$} 
	{ 
		\For{each mini-batch $B$}
		{
			Generate pseudo-labels:\\
			$y_{i}^{pl} = arg max$ ${p_{i}^{teacher}}, i \in B \cap P_{u}$\;
			Train one step:\\
			$w = w - \eta \nabla (l_{seg}(y,p_{l}|w)+
			l_{ent}(p_{u}|w)+ l_{cc}(p^{teacher},p|w)+ {l_{seg}}^{pl}(y^{pl},p_{u}|w))$\;
			Updata Teacher model:\\
			$w^{teacher}_{t} = \alpha \cdot w^{teacher}_{t-1} + (1-\alpha)w^{student}_{t}$\;
		}
	}
\end{algorithm}
\end{document}