  Noise ubiquitously exists in signals due to numerous factors including
physical, electronic, and environmental effects. Traditional methods of
symbolic regression, such as genetic programming or deep learning models, aim
to find the most fitting expressions for these signals. However, these methods
often overlook the noise present in real-world data, leading to reduced fitting
accuracy. To tackle this issue, we propose \textit{\textbf{D}eep Symbolic
Regression against \textbf{N}oise via \textbf{C}ontrastive \textbf{L}earning
(DN-CL)}. DN-CL employs two parameter-sharing encoders to embed data points
from various data transformations into feature shields against noise. This
model treats noisy data and clean data as different views of the ground-truth
mathematical expressions. Distances between these features are minimized,
utilizing contrastive learning to distinguish between 'positive'
noise-corrected pairs and 'negative' contrasting pairs. Our experiments
indicate that DN-CL demonstrates superior performance in handling both noisy
and clean data, presenting a promising method of symbolic regression.
