Interpreting Contrastive Embeddings in Specific Domains with Fuzzy Rules
Free-style text is still one of the common ways in which data is registered in real environments, like legal procedures and medical records. Because of that, there have been significant efforts in the area of natural language processing to convert these texts into a structured format, which standard machine learning methods can then exploit. One of the most popular methods to embed text into a vectorial representation is the Contrastive Language-Image Pre-training model (CLIP), which was trained using both image and text. Although the representations computed by CLIP have been very successful in zero-show and few-shot learning problems, they still have problems when applied to a particula...
| Authors | Javier Fumanal-Idocin, Mohammadreza Jamalifard, Javier Andreu-Perez |
|---|---|
| Primary category | cs.SC |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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