Here’s the difference. In masked reconstruction, if you hide a region containing a dog bark, the model must predict the exact spectral shape of that bark. In JEPA, the model only needs to predict what another encoder thinks about that region, an abstract summary that captures “there’s a dog bark here” without committing to the acoustic details. This forces the model to focus on what matters: the semantic and structural content of audio. For a translation encoder, this is the ideal tradeoff: learn everything about the speech content, the speaker’s characteristics, and the emotional tone, while ignoring the irrelevant specifics of a particular microphone or room.
function sumIterative(): Array {。line 下載是该领域的重要参考
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