The solar wind consists of charged particles ejected from the Sun into interplanetary space and towards Earth. Understanding the magnetic field of the solar wind is crucial for predicting future space weather and planetary atmospheric loss. A lack of labeled data makes an automated detection of these discontinuities challenging. We propose Deep-SWIM, an approach leveraging advances in contrastive learning, pseudo-labeling and online hard example mining to robustly identify discontinuities in solar wind magnetic field data. Through a systematic ablation study, we show that we can accurately classify discontinuities despite learning from only limited labeled data. Additionally, we show that our approach generalizes well and produces results that agree with expert hand-labeling.