Yes, embeddings can often be reused across different tasks, especially when they have been pre-trained on large datasets and capture generalizable features. For instance, word embeddings like Word2Vec or GloVe can be reused in various NLP tasks, such as sentiment analysis, text classification, or machine translation, without needing to be retrained from scratch.
Transfer learning is a key concept here: embeddings that are learned on one task or domain can be fine-tuned for other tasks with relatively little additional training. For example, an image embedding trained for object recognition can be fine-tuned to work for facial recognition or image captioning tasks.
Reusing embeddings saves time and computational resources, as the model can leverage the knowledge captured in the pre-trained embeddings. However, the extent to which embeddings can be reused depends on the similarity between the source and target tasks. In cases where the tasks are very different, it may be necessary to retrain or adjust the embeddings.