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[short] Tango 2: Aligning Diffusion-based Text-to-Audio Generations through Direct Preference Optimization
The paper explores improving text-to-audio generation by focusing on concepts and temporal ordering, using a preference dataset and diffusion-DPO loss to enhance performance over existing models.
https://arxiv.org/abs//2404.09956
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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By Igor Melnyk
5
33 ratings
The paper explores improving text-to-audio generation by focusing on concepts and temporal ordering, using a preference dataset and diffusion-DPO loss to enhance performance over existing models.
https://arxiv.org/abs//2404.09956
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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