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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