MANTA -- Model Adapter Native generations that's Affordable

Fuente: arXiv
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Main Author: Chaurasia, Ansh
Format: Preprint
Published: 2024
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author Chaurasia, Ansh
author_facet Chaurasia, Ansh
contents The presiding model generation algorithms rely on simple, inflexible adapter selection to provide personalized results. We propose the model-adapter composition problem as a generalized problem to past work factoring in practical hardware and affordability constraints, and introduce MANTA as a new approach to the problem. Experiments on COCO 2014 validation show MANTA to be superior in image task diversity and quality at the cost of a modest drop in alignment. Our system achieves a $94\%$ win rate in task diversity and a $80\%$ task quality win rate versus the best known system, and demonstrates strong potential for direct use in synthetic data generation and the creative art domains.
format Preprint
id arxiv_https___arxiv_org_abs_2409_14363
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle MANTA -- Model Adapter Native generations that's Affordable
Chaurasia, Ansh
Artificial Intelligence
Image and Video Processing
The presiding model generation algorithms rely on simple, inflexible adapter selection to provide personalized results. We propose the model-adapter composition problem as a generalized problem to past work factoring in practical hardware and affordability constraints, and introduce MANTA as a new approach to the problem. Experiments on COCO 2014 validation show MANTA to be superior in image task diversity and quality at the cost of a modest drop in alignment. Our system achieves a $94\%$ win rate in task diversity and a $80\%$ task quality win rate versus the best known system, and demonstrates strong potential for direct use in synthetic data generation and the creative art domains.
title MANTA -- Model Adapter Native generations that's Affordable
topic Artificial Intelligence
Image and Video Processing
url https://arxiv.org/abs/2409.14363