Large spectroscopic surveys rely on automated pipelines to deliver homogeneous stellar labels; however, a substantial fraction of observations are carried out at a low signal-to-noise ratio (S/N) where label estimates become imprecise or are omitted. In APOGEE, these low-S/N spectra visits tend to sample faint and distant populations (i.e. the bulge, outer halo, and satellite systems), while still encoding recoverable chemical information. We present TwinSpecNet (TSN), a paired-learning framework that exploits APOGEE's multi-visit observing strategy: by training on empirical low- and high-S/N spectral twins of the same stars, TSN learns to suppress stochastic noise while preserving the ASPCAP label scale. TSN employs a Vision Transformer encoder with dual objectives: reconstructing high-S/N flux from low-S/N visits and predicting stellar parameters and abundances with calibrated uncertainties. TSN reduces label scatter relative to visit-level ASPCAP for S/N<60 visits. It reproduces the ASPCAP scale with residual scatters of sigma~19K in Teff, sigma~0.06dex in logg, and sigma~0.03dex in Fe/H. TSN tightens intra-cluster abundance dispersions, recovers cleaner chemical sequences in inner-disk and bulge and satellite samples, and improves C/N-based age precision for APOKASC giants from 1.70 to 1.49Gyr. By learning survey-specific noise patterns from repeated observations, TSN demonstrates how empirical paired learning can extend the chemical reach of existing spectroscopic data, providing a template that is applicable to other multi-visit surveys.