A More Composable Approach to Embedding Training
victormustar · x · 2026-07-14
The author proposes a training approach to improve embeddings:
- During training, synthetic data is generated for attributes; for example, breaking bike down into components like vehicle / wheel / balance / pedals.
- A loss is then added at the embedding layer so that the sum of these component vectors closely approximates the vector of the overall concept bike.
The core goal is to make embeddings more composable and interpretable, rather than just learning black-box similarities.
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